Hierarchical machine learning techniques for identifying molecular categories from expression data

ABSTRACT

Described herein in some embodiments is a method comprising: obtaining expression data previously obtained by processing a biological sample obtained from a subject; processing the expression data using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of machine learning classifiers comprising first and second machine learning classifiers corresponding to the first and second molecular categories; and identifying, using at least some of the machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. 119(e) of U.S. provisional patent application No. 63/121,863, titled “MACHINE LEARNING TECHNIQUES FOR GENE EXPRESSION DATA AND GENOMIC DATA ANALYSIS”, filed on Dec. 4, 2020, which is incorporated by reference herein in its entirety.

FIELD

Aspects of the technology described herein relate to machine learning techniques for analyzing DNA and/or RNA expression data obtained from a biological sample obtained from a subject known to have, suspected of having or at risk of having cancer.

BACKGROUND

Some cancers can be classified by the organ or tissue in which they originated. A “primary tumor” refers to a tumor that forms when a cell or cells undergo oncogenesis in an organ or tissue in which they are present and have not metastasized to that location from another location. The organ or tissue in which the primary tumor forms may be referred to as the “primary site of origin” or the “primary site”. Metastasis occurs when cancer cells have spread from the primary site of origin to one or more other parts of the body (e.g., secondary sites). The resulting tumors may be referred to as “metastatic tumors”.

SUMMARY

Some embodiments provide for a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises using at least one computer hardware processor to perform: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

Some embodiments provide for a system, comprising: at least one computer hardware processor; at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

In some embodiments, the RNA expression data further comprises third RNA expression data for a third set of genes different from the first and second sets of genes, the hierarchy of molecular categories further comprises a third molecular category that is a child of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers further comprises a third RNA-based machine learning classifier corresponding to the third molecular category, the processing further comprises processing the third RNA expression data using the third RNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, and identifying the at least one candidate molecular category for the biological sample is performed using the third output.

In some embodiments, the RNA expression data further comprises fourth RNA expression data for a fourth set of genes different from the first and second sets of genes, the hierarchy of molecular categories further comprises a fourth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers further comprises a fourth RNA-based machine learning classifier corresponding to the fourth molecular category, the processing further comprises processing the fourth RNA expression data using the fourth RNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, and identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.

In some embodiments, the RNA expression data further comprises fifth RNA expression data for a fifth set of genes different from the first, second, and fourth sets of genes, the hierarchy of molecular categories further comprises a fifth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers further comprises a fifth RNA-based machine learning classifier corresponding to the fifth molecular category, wherein the processing further comprises processing the fifth RNA expression data using the fifth RNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.

In some embodiments, the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category.

In some embodiments, the parent molecular category is a breast cancer molecular category, wherein the first molecular category is a basal breast cancer molecular category, and wherein the second molecular category is a non-basal breast cancer molecular category.

In some embodiments, the parent molecular category is a molecular category selected from Table 2, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories shown in FIGS. 7A-1, 7A-2, and 7A-3 .

In some embodiments, processing the first RNA expression data using the first RNA-based machine learning classifier comprises: obtaining first RNA features from the first RNA expression data; and applying the first RNA-based machine learning classifier to the first RNA features to obtain the first output.

In some embodiments, the first RNA expression data comprises first expression levels for the first set of genes, wherein obtaining the first RNA features from the first RNA expression data comprises ranking at least some genes in the first set of genes based on the first expression levels to obtain a first gene ranking, the first gene ranking including values identifying relative ranks of the at least some genes in the gene ranking, wherein the values are different from the first expression levels, and applying the first RNA-based machine learning classifier to the first RNA features comprises applying the first RNA-based machine learning classifier to the first gene ranking to obtain the first output.

In some embodiments, processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output comprises processing the first RNA expression data to obtain a first probability that the first molecular category is a first candidate molecular category for the biological sample, and processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output comprises processing the second RNA expression data to obtain a second probability that the second molecular category is a second candidate molecular category for the biological sample.

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold; and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold.

In some embodiments, the method further comprises excluding the first molecular category from the at least one candidate molecular category identified for the biological sample when the first probability does not exceed the threshold.

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to the second probability; and identifying the first molecular category as a candidate molecular category of the at least one candidate molecular category for the biological sample when the first probability exceeds the second probability.

In some embodiments, the first molecular category is a molecular category selected from molecular categories listed in Table 2. In some embodiments, the first set of genes comprises at least 10 genes listed in at least one of Table 3 corresponding to the first molecular category.

In some embodiments, the first molecular category is associated with at least one international classification of diseases (ICD) code.

In some embodiments, the method further comprises: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject; and processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to the hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs, wherein the hierarchy of DNA-based machine learning classifiers is different from the hierarchy of RNA-based machine learning classifiers, wherein the identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the DNA-based machine learning classifier outputs.

In some embodiments, processing the DNA expression data comprises: obtaining one or more DNA features using the DNA expression data; and applying at least one DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers to at least some of the DNA features to obtain the DNA-based machine learning classifier outputs.

In some embodiments, the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene. In some embodiments, the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene. In some embodiments, the one or more DNA features comprise a feature indicating tumor mutational burden for the biological sample. In some embodiments, the one or more DNA features comprise one or more features indicating a normalized copy number for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data. In some embodiments, the one or more DNA features comprise one or more features indicating loss of heterozygosity (LOH) for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data. In some embodiments, the one or more DNA features comprise one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes. In some embodiments, the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene of the respective plurality of genes. In some embodiments, the one or more DNA features comprises a feature indicating ploidy for the biological sample. In some embodiments, the one or more DNA features comprise a indicating whether the DNA expression data indicates presence of microsatellite instability (MSI). In some embodiments, the one or more DNA features comprise at least ten features listed in Table 5.

In some embodiments, the identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of a purity of the biological sample and/or data indicative of a site form which the biological sample was obtained.

In some embodiments, the hierarchy of DNA-based machine learning classifiers comprises at least 10 DNA-based machine learning classifiers.

In some embodiments, a first DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier. In some embodiments, each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, and a logistic regression classifier.

In some embodiments, the method further comprises: receiving an indication of a clinical diagnosis of the biological sample; and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample.

In some embodiments, the method further comprises: generating, using the hierarchy of molecular categories, a graphical user interface (GUI) including a visualization indicating the at least one molecular category identified for the biological sample.

In some embodiments, first molecular category of the hierarchy of molecular categories is one of a neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma.

In some embodiments, the hierarchy of RNA-based machine learning classifiers comprises at least 10 RNA-based machine learning classifiers. In some embodiments, the first RNA-based machine learning classifier is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier. In some embodiments, each RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.

In some embodiments, the first RNA expression data comprises expression levels for between 20 and 300 genes.

In some embodiments, the subject has, is suspected of having or is at risk for having cancer. In some embodiments, the biological sample is a sample of a cancer of unknown primary (CUP) tumor.

In some embodiments, the method further comprises identifying at least one anti-cancer therapy for the subject based on the identified at least one molecular category. In some embodiments, the method further comprises administering the at least one anti-cancer therapy.

Some embodiments provide for a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises using at least one computer hardware processor to perform: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data and second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

Some embodiments provide for a system, comprising at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample;

processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject. The method comprises: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data and second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.

In some embodiments, the DNA expression data further comprises third DNA expression data, the hierarchy of molecular categories further comprises a third molecular category that is a child of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers further comprises a third DNA-based machine learning classifier corresponding to the third molecular category, the processing further comprises processing the third DNA expression data using the third DNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, and identifying the at least one candidate molecular category for the biological sample is performed using the third output.

In some embodiments, the DNA expression data further comprises fourth DNA expression data, the hierarchy of molecular categories further comprises a fourth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers further comprises a fourth DNA-based machine learning classifier corresponding to the fourth molecular category, the processing further comprises processing the fourth DNA expression data using the fourth DNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, and identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.

In some embodiments, the DNA expression data further comprises fifth DNA expression data, the hierarchy of molecular categories further comprises a fifth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers further comprises a fifth DNA-based machine learning classifier corresponding to the fifth molecular category, the processing further comprises processing the fifth DNA expression data using the fifth DNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, and identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.

In some embodiments, the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category.

In some embodiments, the parent molecular category is a breast cancer molecular category, the first molecular category is a basal breast cancer molecular category, and the second molecular category is a non-basal molecular category.

In some embodiments, the parent molecular category is a molecular category selected from Table 2, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories shown in FIGS. 7A-1, 7A-2, and 7A-3 .

In some embodiments, processing the first DNA expression data using the first DNA-based machine learning classifier comprises: obtaining one or more first DNA features from the first DNA expression data; and applying the first DNA-based machine learning classifier to the first DNA features to obtain the first output.

In some embodiments, the one or more first DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene. In some embodiments, the one or more first DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene. In some embodiments, the one or more first DNA features comprise a feature indicating tumor mutational burden for the biological sample. In some embodiments, the one or more DNA features comprise one or more features indicating a normalized copy number for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data. In some embodiments, the one or more DNA features comprise one or more features indicating loss of heterozygosity (LOH) for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data. In some embodiments, the one or more DNA features comprise one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes. In some embodiments, the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene of the respective plurality of genes. In some embodiments, the one or more DNA features comprises a feature indicating ploidy for the biological sample. In some embodiments, the one or more DNA features comprise a indicating whether the DNA expression data indicates presence of microsatellite instability (MSI). In some embodiments, the one or more first DNA features comprise at least 10 features listed in Table 5 corresponding to the first molecular category.

In some embodiments, wherein processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output comprises processing the first DNA expression data to obtain a first probability that the first molecular category is a first candidate molecular category for the biological sample, and wherein processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output comprises processing the second DNA expression data to obtain a second probability that the second molecular category is a second candidate molecular category for the biological sample.

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold; and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold.

In some embodiments, the method further comprises excluding the first molecular category from the at least one candidate molecular category identified for the biological sample when the first probability does not exceed the threshold.

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to the second probability; and identifying the first molecular category as a candidate molecular category of the at least one candidate molecular category for the biological sample when the first probability exceeds the second probability.

In some embodiments, the first molecular category is a molecular category selected from molecular categories listed in Table 2.

In some embodiments, the first molecular category is associated with at least one international classification of diseases (ICD) code.

In some embodiments, the method further comprises: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject; and processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to the hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs, wherein the hierarchy of RNA-based machine learning classifiers is different from the hierarchy of DNA-based machine learning classifiers, wherein the identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the RNA-based machine learning classifier outputs.

In some embodiments, processing the RNA expression data comprises: obtaining RNA features using the RNA expression data; and applying at least one RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers to at least some of the RNA features to obtain the RNA-based machine learning classifier outputs.

In some embodiments, the RNA expression data comprises expression levels for at least one set of genes, obtaining the RNA features using the RNA expression data comprises ranking genes in the at least one set of genes based on the expression levels to obtain at least one gene ranking, the at least one gene ranking including values identifying relative ranks of the genes in the at least one gene ranking, wherein the values are different from the expression levels, and wherein applying the at least one RNA-based machine learning classifier to the at least some of the RNA features comprises applying the RNA-based machine learning classifier to the at least one gene ranking to obtain the RNA-based machine learning classifier outputs.

In some embodiments, identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of a purity of the biological sample and/or based on data indicative of a site from which the biological sample was obtained.

In some embodiments, the hierarchy of RNA-based machine learning classifiers comprises at least 10 RNA-based machine learning classifiers.

In some embodiments, a first RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier. In some embodiments, each RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.

In some embodiments, the RNA expression data comprises expression levels for between 20 and 300 genes.

In some embodiments, the method further comprises: receiving an indication of a clinical diagnosis of the biological sample; and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample.

In some embodiments, the method further comprises generating, using the hierarchy of molecular categories, a graphical user interface (GUI) including a visualization indicating the at least one molecular category identified for the biological sample.

In some embodiments, the first molecular category of the hierarchy of molecular categories is one of neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma.

In some embodiments, the hierarchy of DNA-based machine learning classifiers comprises at least 10 DNA-based machine learning classifiers.

In some embodiments, the first DNA-based machine learning classifier is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.

In some embodiments, each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.

In some embodiments, the subject has, is suspected of having or is at risk for having cancer. In some embodiments, the biological sample is a sample of a cancer of unknown primary (CUP) tumor.

In some embodiments, the method further comprises identifying at least one anti-cancer therapy for the subject based on the identified at least one molecular category. In some embodiments, the method further comprises administering the at least one anti-cancer therapy.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a diagram depicting an illustrative technique 100 for identifying a candidate molecular category for a biological sample using a hierarchy of machine learning classifiers, according to some embodiments of the technology described herein.

FIG. 1B is a screenshot of an example report indicating candidate molecular categories identified using illustrative technique 100, according to some embodiments of the technology described herein.

FIG. 2A shows an illustrative hierarchy 200 of molecular categories, according to some embodiments of the technology described herein.

FIG. 2B-1 is a diagram depicting an illustrative technique 220 for processing expression data to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein.

FIG. 2B-2 is a diagram depicting an example 230 of illustrative technique 250 for processing expression data to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein.

FIG. 2C shows an illustrative diagram 250 of a two-class classifier, optionally a multi-class classifier, used to determine whether a molecular category is a candidate molecular category for a biological sample, according to some embodiments of the technology described herein.

FIG. 2D illustrates identifying a candidate molecular category for a biological sample using machine learning classifiers at the same level of a hierarchy of machine learning classifiers, according to some embodiments of the technology described herein.

FIG. 3 is a block diagram of a system 300 including example computing device 304 and software 310, according to some embodiments of the technology described herein.

FIG. 4A shows a flowchart of an illustrative process 400 for identifying at least one candidate molecular category for a biological sample using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein.

FIG. 4B shows a flowchart of an illustrative process 420 for identifying at least one candidate molecular category for a biological sample using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein.

FIG. 4C shows a flowchart of an illustrative process 440 for identifying at least one candidate molecular category for a biological sample using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein.

FIG. 5A-1 is an example 500 for processing RNA expression data obtained from a biological sample to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

FIG. 5A-2 is an example 550 for processing DNA expression data obtained from a biological sample to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

FIG. 5B illustrates an example 570 for combining the output of the hierarchy of RNA-based machine learning classifiers with the output of the hierarchy of DNA-based machine learning classifiers to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

FIGS. 5C-5D shows an example of correcting for probabilities output by machine learning classifiers of the hierarchy of machine learning classifiers, according to some embodiments of the technology described herein.

FIG. 6A is a diagram showing example RNA expression data and example RNA features obtained from the RNA expression data, according to some embodiments of the technology described herein.

FIG. 6B is a diagram showing example DNA expression data and example DNA features obtained from the DNA expression, according to some embodiments of the technology described herein.

FIGS. 7A-1-7A-3 show an example hierarchy 700 of molecular categories, according to some embodiments of the technology described herein.

FIG. 7B-1-7B-5 show an example hierarchy 750 of molecular categories, according to some embodiments of the technology described herein.

FIG. 8A shows a flowchart of an illustrative process 800 for training an RNA-based machine learning classifier to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein.

FIG. 8B shows a flowchart of an illustrative process 850 for training a DNA-based machine learning classifier to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein.

FIG. 9A is a plot showing that tumor samples belonging to a same molecular category share similar gene expression profiles, according to some embodiments of the technology described herein.

FIG. 9B is a diagram illustrating the performance of the machine learning techniques developed by the inventors, according to some embodiments of the technology described herein.

FIG. 9C is a diagram illustrating the performance of an RNA-based machine learning classifier developed by the inventors, according to some embodiments of the technology described herein.

FIG. 9D shows precision-recall curves illustrating the performance of the RNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9E shows receiver operating characteristic (ROC) curves illustrating performance of the RNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9F is a diagram illustrating the performance of a DNA-based machine learning classifier developed by the inventors, according to some embodiments of the technology described herein.

FIG. 9G shows precision-recall curves illustrating the performance of the DNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9H shows receiver operating characteristic (ROC) curves illustrating performance of the DNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 10 depicts an illustrative implementation of a computer system that may be used in connection with some embodiments of the technology described herein.

DETAILED DESCRIPTION

Aspects of the disclosure relate to machine learning techniques for analyzing expression data obtained from a biological sample obtained from a subject that may have been diagnosed with cancer of unknown primary “CUP” and/or another type of cancer and identifying one or more molecular categories for the biological sample based on results of the machine learning analysis. The machine learning techniques involve processing DNA and/or RNA expression data with a set of hierarchically organized machine learning classifiers, corresponding to a hierarchy of molecular categories, to identify one or more molecular categories for the biological sample. In turn, the identified molecular category or categories may be used for numerous applications including, but not limited to, identifying or facilitating identification of one or more therapeutically effective treatments for the subject (which can subsequently be administered), identifying one or more clinical trials in which the subject may be enrolled, generating a more accurate than previously possible characterization of the tumor's molecular characteristics, and performing one or more quality control processes on the biological sample (e.g., in a laboratory environment the techniques may be used to confirm whether a biological sample labeled with an alleged primary site is in fact a tumor sample that originated at that primary site).

As described above, one important application of the techniques described herein is analyzing expression data obtained from a biological sample obtained from a subject that may have been diagnosed with cancer of unknown primary. “Cancer of unknown primary (CUP)” refers to a group of one or more metastatic tumors for which the primary site of origin cannot be determined at the time of diagnosis of the subject. CUP is quite common and constitutes 3%-5% of all cancer diagnoses, and presents several clinical challenges. For example, CUP tumors are generally aggressive, associated with poor overall survival (OS), and have unpredictable metastatic patterns. Typically, CUP is divided into two categories: about 20% of CUP is characterized as having a good prognosis, and about 80% of CUP is characterized as having a poor prognosis. Treatment of CUP historically comprises either locoregional or systemic administration of platinum-based chemotherapy, or empirical chemotherapy and combinations of platinum or taxane.

Conventional techniques for identifying effective therapies for a CUP tumor involve attempting to identify a primary site for the CUP tumor and then treat the CUP tumor with one or more therapies known to be effective for tumors that originate from the identified primary site. However, such conventional techniques suffer from numerous problems.

First, the lack of differentiation of many CUP tumors makes identification of a primary site of origin challenging. It is difficult to identify the primary site of origin of a CUP tumor because the cells bear little to no resemblance to the normal cells from which they originated, which is the case in a large percentage of CUP cases. (This is also the case in instances of rare malignant cancers, where there is insufficient data to support an identification of the primary site of origin.) Indeed, conventional clinical diagnostic methods, such as blood and biochemical analyses, radiological analyses, and immunohistochemical analysis have had only limited success in characterizing or identifying the origin of CUP tumors, and are often limited to identification of more differentiated CUP tumors. Similarly, tissue of origin classifiers based on genetic information have also been limited in their prognostic value for highly undifferentiated CUP. As such, the conventional approach of identifying an effective therapy for treating a CUP tumor (or another cancer) based on an identified primary site is not possible when the primary site cannot be determined accurately or even at all.

Second, even in cases where it is possible to identify a primary site of origin of the tumor, that identification may not be sufficiently specific to identify an effective treatment for the tumor. A more specific characterization may be needed to identify highly-effective tumor specific therapies. Indeed, there can be important differences between cells originating from the same primary site (e.g., breast cancer cells can be further classified into basal breast cancer cells and non-basal breast cancer cells based on their gene expression) and these differences can impact the selection of the most effective therapy.

Moreover, in some situations, cancer cells originating from different primary sites (e.g., site “A” and site “B”) may be, in fact, sufficiently similar to one another such that a treatment for a tumor having primary site “A” may be used, effectively, to treat a tumor having primary site “B”. Identifying such treatments for a subject is not possible using conventional primary site identification techniques because they would not identify alternative sites (associated with effective therapies) where tumors with molecular characteristics similar to that of the subject's tumor can originate. As one example, adenocarcinomas of colon and rectum demonstrate similar molecular profiles, although they are associated with different primary sites. Similar tendencies have been observed in various types of gynecological or squamous cell cancers.

The inventors have recognized that in order to address the drawbacks of conventional techniques of identifying treatments based on primary site identification, it is better to instead characterize a tumor sample as belonging to one more “molecular categories”, in a hierarchy of such molecular categories, based on the tumor's molecular features (e.g., features derived from DNA and/or RNA expression data obtained from the tumor) and to identify effective treatments for the tumor based on the molecular categories so identified.

A “molecular category” refers to a category or group of biological samples (e.g., tumor samples) that have similar molecular features (e.g., features derived from expression data). Molecular categories may be organized into a hierarchy of molecular categories in which molecular categories at different levels of the hierarchy have differing degrees of specificity —molecular categories at higher levels of the hierarchy are broader categories having lower specificity, while molecular categories at lower levels of the hierarchy are narrower categories having higher specificity. Numerous examples of such hierarchies and their constituent molecular categories are provided herein including with reference to FIGS. 1B, 2A 7A-1-7A-3, and 7B-1-7B-5.

The inventors have developed hierarchies of molecular categories and machine learning techniques for identifying, from DNA and/or RNA expression data obtained from a tumor sample, one or more molecular categories for the tumor in a particular hierarchy of molecular categories. The machine learning techniques involve processing the DNA and/or RNA expression data obtained from a tumor sample with at least one hierarchy of machine learning classifiers that corresponds to a hierarchy of molecular categories and to identify one or more candidate molecular categories for the tumor sample based on output generated by the machine learning classifiers in the hierarchy or hierarchies. As described herein, the identified candidate molecular categories may be used to identify one or more therapies for the subject and have many other uses including, but not limited to, identifying one or more clinical trials in which the subject may enroll, providing a clinician with a graphical user interface (GUI) presenting a visualization of tumor characteristics (e.g., by presenting a visualization of the hierarchy of molecular categories and, among them, visually highlighting the identified molecular category or categories), and performing quality control on biological samples in a laboratory environment.

For example, some embodiments provide for a method for identifying at least one candidate molecular category for a biological sample. The method includes: (a) obtaining expression data (e.g., RNA and/or DNA expression data) previously obtained from a biological sample obtained from a subject, (b) processing the expression data using at least one hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs, and (c) identifying, using the machine learning classifier outputs, at least one candidate molecular category for the biological sample. In some embodiments, the at least one identified candidate molecular category may be used to identify a therapy for the subject, which therapy may then be administered. In some embodiments, processing the expression data using the at least one hierarchy of machine learning classifiers includes processing expression data that is specific to a particular molecular category to determine whether the molecular category should be identified as a candidate molecular category for the biological sample. In some embodiments, a machine learning classifier in the at least one hierarchy of machine learning classifiers is trained to determine whether to identify a particular molecular category as a candidate molecular category for the biological sample based on the specific expression data for that molecular category.

The techniques developed by the inventors and described herein address the above-described shortcomings of conventional methods for identifying therapies for treating a tumor based on identifying a primary site of origin for the tumor.

The techniques described herein identify one or more molecular categories, in a hierarchy of molecular categories, for a tumor based on a tumor's molecular features. As a result, in cases where it is difficult to identify a primary site of origin for a tumor (e.g., when the tumor is undifferentiated), it may nonetheless be possible to identify a molecular category for the tumor (e.g., it may not be possible to identify that the tumor originated in the ovaries, but it may nonetheless be possible to identify that the tumor belongs to the molecular category of gynecological cancers of which ovarian cancer is a subcategory). Even though the molecular category so identified is not limited to tumors from a specific and particular site (e.g., ovaries) and may be broad enough to include multiple different primary sites (e.g., ovaries and uterus), it may nevertheless be sufficient to identify a treatment for the tumor. For example, some therapies may work for both uterine and ovarian cancers because of the molecular similarity among these cancers and, as such, a treatment may be identified using the techniques described herein, whereas using conventional techniques this would not be possible (e.g., because a conventional classifier trained to identify primary sites would fail to identify the primary site with high confidence and its output would be discarded).

On the other hand, there may be cases where the molecular features of a tumor (e.g., of a highly differentiated tumor) may be sufficiently informative so as to identify a histological subtype of a tumor, which enables the identification of treatments that are highly specific to the tumor and have the greatest potential in effectively treating the tumor. For example, a conventional technique may identify, for a differentiated tumor, its primary site as breast tissue and, therefore, that the tumor is breast cancer. However, the techniques described herein may be used to go further and to identify histological subtypes of the tumor (e.g., whether the breast cancer is non-Basal breast cancer or basal breast cancer), which can be used to further tailor the treatment selected.

Consequently, the techniques developed by the inventors provide for more accurate characterization of tumor samples than previously possible using conventional methods. This technology therefore provides an improved diagnostic tool, which can be used to improve the way in which treatments are identified for patients thereby improving clinical outcomes. The techniques described herein allow for the identification of therapies where conventional approaches, based on primary site of origin identification, simply fail to do so. And even where such conventional techniques are able to identify a primary site of origin, the techniques developed by the inventors can go further and identify a histological subtype of the tumor which enables the identification of more tumor-specific treatments than possible merely based on an identified primary site of origin.

In addition to identifying therapies for a subject based on the molecular categories identified using the techniques described herein, one or more clinical trials may be identified for the subject using the identified molecular categories (and, for example, biomarkers associated with the molecular categories; the biomarkers being, for example, the features used as input by the classifiers used to determine that the sample is to be associated with the identified molecular categories).

The techniques described herein may be implemented as part of a software diagnostic tool, which may be used to present medical professionals with information characterizing the molecular features of a patient's tumor. For example, the techniques described herein may be used to identify one or more molecular categories for a patient's tumor (including, for example, with associated probabilities and/or confidences). In turn, the software tool may use this information to generate a visualization of the hierarchy of molecular categories and a visual indication, within the hierarchy, of the molecular categories identified for the tumor (e.g., using color, shading, size, or any other suitable visual cue, as aspects of the technology described herein are not limited in this respect). Additionally, the visualization may include information about confidences of the machine learning classifier(s) used to identify the molecular categories. An example is shown in FIG. 1B, which is further described below.

Additionally or alternatively, the techniques described herein may be utilized in the context of quality control processes in a laboratory environment. For example, a sequencing laboratory may receive a biological sample together with information about the biological sample. Aside from an identifier and/or tracking number, such information may include information about the characteristics of the biological sample (e.g., the tissue source, cancer type, cancer grade, etc.). However, due to errors, it is possible that the biological sample provided does not actually have these characteristics (e.g., due to an error where patient samples are switched, mislabeled, wrong information is provided, etc.). Another application of the techniques described herein is to quality control analysis in a data analysis setting. For example, a patient's sequencing data (e.g., reads, aligned reads, expression levels, etc.) may be provided as input to a data processing pipeline. However, if that sequencing data does not correspond to the alleged source (e.g., it comes from a different patient due to an error), the results of the analysis are likely meaningless.

In some embodiments, quality control may be performed by comparing an asserted characteristic of a biological sample to a predicted characteristic determined using the techniques described herein. When the asserted characteristic and the predicted characteristic match (e.g., are the same or are within a tolerated difference) and/or are consistent with one another, then it may be determined that a quality control check has been satisfied. On the other hand, if the predicted and asserted characteristics do not match, then further action may need to be taken. For example, further analysis of the biological sample may be performed, the biological sample may be rejected, a data processing pipeline may be stopped or not executed (thereby saving valuable and costly computational resources), a laboratory operator and/or other party (e.g., clinician, staff, etc.) may be notified of a potential discrepancy (e.g., by an e-mail alert, a message, a report, an entry in a log-file, etc.).

As one example, the techniques described herein may be used to identify a molecular category from expression data obtained from a sample and that category may be compared with the stated cancer type and/or primary site for the tumor provided with the sample. When the identified molecular category is consistent with the stated cancer type and/or primary site (e.g., the primary site is identified as breast tissue and the molecular category identified is non-basal breast cancer), then this type of quality control check may be passed. On the other hand, when the identified molecular category is inconsistent with the stated cancer type and/or primary site (e.g., the identified molecular category is clear cell carcinoma, but the type of cancer is identified is melanoma), then this type of quality control check may be failed. Further analysis may be performed.

As described herein, the techniques developed by the inventors provide for more accurate characterization of tumor samples than previously possible using conventional methods. Multiple aspects of the developed technology described herein enable this to occur.

One such aspect is the architecture of the machine learning models used to identify one or more molecular categories for a biological sample. In some embodiments, the techniques involve using a hierarchy of machine learning classifiers that corresponds to the hierarchy of molecular categories. In some embodiments, individual machine learning classifiers in the hierarchy of machine classifiers correspond to respective individual molecular categories in the hierarchy of molecular categories (e.g., as shown in FIGS. 2B-1 and 2B-2 among others). Thus, in some embodiments, a separate machine learning classifier is trained and used to determine whether to identify a particular, respective molecular category for the biological sample, which improves overall accuracy of identification of molecular categories (e.g., as compared to conventional methods that rely on a single multi-class classifier to identify one of a plurality of primary sites from expression data from a sample). The use of a hierarchy of machine learning classifiers allows for the identification of multiple candidate molecular categories of different specificity. Accordingly, molecular categories identified at a general level of the hierarchy may be used to inform identification of molecular categories at a more specific level of the hierarchy, contributing to the accuracy and performance of the techniques described herein.

Relatedly, the use of a hierarchy of machine learning classifiers provides an important computational advantage to using separate, non-hierarchically organized, classifiers for various primary sites and/or categories. The advantage is that decisions made by classifiers at a higher level in the classifier hierarchy may be used to identify a relevant branch in the hierarchy for further processing and, therefore, eliminate the need to invoke and perform any processing using machine learning classifiers in one or more other branches in the hierarchy, thereby saving significant computational resources (e.g., processing resources, network resources utilized by having to transmit expression data, which may be voluminous) and enabling faster processing of the expression data to identify the relevant molecular categories. For example, if the machine learning classifier corresponding to the “Adenocarcinoma” category in FIG. 7A-2 outputs an indication that the tumor is likely an Adenocarcinoma sample and not likely to be anything else, it may not be necessary to invoke machine learning classifiers associated with categories in other branches in the hierarchy (e.g., with the “Glioma”, “Squamous Cell Carcinoma”, and “Neuroendocrine” branches of the hierarchy of molecular categories).

In some embodiments, the techniques developed by the inventors may utilize multiple hierarchies of machine learning classifiers to identify candidate molecular categories for the biological sample using different types of expression data. For example, a first hierarchy may include RNA-based machine learning classifiers trained to identify candidate molecular categories based on RNA expression data (e.g., using features derived from the RNA expression data and/or the RNA expression data itself) obtained from the biological sample, while a second hierarchy may include DNA-based machine learning classifiers trained to identify candidate molecular categories based on DNA expression data (e.g., using features derived from the RNA expression data and/or the RNA expression data itself) obtained from the biological sample. Using multiple hierarchies of machine learning classifiers allows the techniques to cross-check identified candidate molecular categories and accounts for deficiencies that might be associated with either the RNA expression data or the DNA expression data. Thus, in some embodiments, only one hierarchy of machine learning classifiers may be used (e.g., using only the hierarchy of RNA-based machine learning classifiers or only the hierarchy of DNA-based machine learning classifiers), but not both. In other embodiments, both the RNA-based and DNA-based hierarchies may be used. When both are used, they may be used independently of one another. In such cases their results may be compared with one another for cross-checking purposes. Alternatively, the numerical outputs generated by classifiers in both hierarchies may be combined (sometimes termed “fused”) as described herein, including with reference to FIG. 5B.

Another aspect of the approach developed by the inventors that contributes to its accuracy and robustness is the use of features (e.g., features derived from DNA and/or RNA expression data, which features may include the DNA and/or RNA expression data itself, in some embodiments) specified a priori for each molecular category to determine whether to identify the molecular category as a candidate molecular category for the biological sample. For example, RNA expression data for a specific set of genes for a particular molecular category may be processed using a machine learning classifier trained to predict whether a particular molecular category should be identified for the biological sample. The RNA expression data may be first processed to obtain a set of features specified a priori for the particular molecular category (e.g., gene rankings for a set of genes associated with the molecular category, the gene rankings obtained by numerically ranking the expression levels for genes in the set of genes) and this set of features may be provided as input to a specific machine learning classifier for that specific molecular category. As another example, DNA expression data may be used to obtain a specific set of DNA features (e.g., features indicating the presence of gene mutations, presence of genes, copy number alterations, loss of heterozygosity (LOH), ploidy, tumor mutational burden, presence of gene fusions, microsatellite instability (MSI) status, etc.) for a particular molecular category. Then these DNA features may be provided as input to and be processed using a machine learning classifier trained to predict whether the molecular category is a candidate molecular category for the biological sample. In some embodiments, the use of specific features tailored for each particular molecular category allows the techniques developed by the inventors to leverage domain-specific knowledge to distinguish among molecular categories, even when they share similar molecular features, contributing to the success of the techniques described herein. Examples of RNA and DNA features used by RNA-based and DNA-based machine learning classifiers, respectively, are provided herein.

Accordingly, some embodiments provide for computer-implemented techniques for identifying at least one candidate molecular category for a biological sample obtained from a subject. The techniques include: (a) obtaining RNA expression data obtained by processing (e.g., sequencing) the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data (e.g., first RNA expression levels) for a first set of genes and second RNA expression data (e.g., second RNA expression levels) for a second set of genes different from the first set of genes; (b) processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers (e.g., the hierarchy of RNA-based machine learning classifiers 500 shown in FIG. 5A-1 ) corresponding to a hierarchy of molecular categories (e.g., the hierarchy of molecular categories 200 shown in FIG. 2A) to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category (e.g., represented by node 202 shown in FIG. 2A) and first and second molecular categories (e.g., represented by nodes 204 b and 204 a of FIG. 2A) that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers (e.g., classifiers 513 b and 514 b shown in FIG. 5A-1 ) corresponding to the first and second molecular categories, the processing comprising: (i) processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output (e.g., a probability or likelihood or other numerical or categorical value) indicative of whether the first molecular category is a candidate molecular category for the biological sample; (ii) processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output (e.g., a probability or likelihood or other numerical or categorical value) indicative of whether the second molecular category is a candidate molecular category for the biological sample; and (c) identifying, using at least some of the RNA-based machine learning classifier outputs (e.g., probabilities 535, 536, and 537 shown in FIG. 5A-1 ) including the first output and the second output, at least one candidate molecular category.

The at least one candidate molecular category may include one or multiple molecular categories. When multiple molecular category candidates are included, they may include multiple molecular categories at different levels of the hierarchy (e.g., indicating a most likely molecular category and its ancestors—parent, grandparent, etc. —in the hierarchy). Additionally or alternatively, when multiple molecular category candidates are included, they may include multiple molecular categories at the same level in the hierarchy (e.g., indicating multiple potential alternative molecular categories for the biological sample and their respective probabilities, likelihood or other numerical or categorical values).

In some embodiments, the first molecular category is associated with at least one international classification of diseases (ICD) code. For example, the first molecular category may be associated with at least one ICD code, at least two ICD codes, at least five ICD codes, at least 10 ICD codes, or between 1 and 10 ICD codes. Example associations of molecular categories and ICD codes are shown in Table 1 herein.

In some embodiments, the hierarchy of molecular categories may be stored using one or more data structures having one or more fields storing information about the hierarchy of molecular categories. For example, the fields may store information indicating, for each category in the hierarchy, its relationship to one or more other categories in the hierarchy (e.g., indicating a parent molecular category and/or one or more child molecular categories), one or more ICD codes associated with the category, one or more histological cancer subtypes associated with the category, one or more treatments known to be therapeutically effective for the category, and/or any other suitable information, as aspects of the technology described herein are not limited in this respect.

In some embodiments, the hierarchy of machine learning classifiers (e.g., hierarchy of DNA-based machine learning classifiers or the hierarchy of RNA-based machine learning classifiers) may be stored in any suitable way. Each of the machine learning classifiers may comprise program code that, when executed, performs classification using the machine learning classifier's inputs, the machine learning classifier's parameters, the machine learning classifier's hyperparameters, and/or any other suitable configuration information. The hierarchical relationships among the machine learning classifiers may be stored using one or more data structures having one or more fields storing information about the hierarchy. For example, the fields may store information indicating, for each machine learning classifier in the hierarchy, its relationship to one or more other machine learning classifiers in the hierarchy (e.g., indicating a parent machine learning classifier and/or one or more child machine learning classifiers), a respective category in the hierarchy of molecular categories to which the classifier corresponds, and/or any other suitable information, as aspects of the technology described herein are not limited in this respect.

In some embodiments, the RNA expression data further comprises third RNA expression data for a third set of genes different from the first and second sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a third molecular category (e.g., represented by node 204 c) that is a child of the parent molecular category in the hierarchy of molecular categories. In some embodiments, the hierarchy of RNA-based machine learning classifiers further comprises a third RNA-based machine learning classifier (e.g., RNA-based machine learning classifier 515 c) corresponding to the third molecular category. In some embodiments, the processing further comprises processing the third RNA expression data using the third RNA-based machine learning classifier (e.g., by processing the third RNA expression data to obtain RNA features 515 a with RNA classifier 515 b) to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the third output.

In some embodiments, the RNA expression data further comprises fourth RNA expression data for a fourth set of genes different from the first and second sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a fourth molecular category (e.g., represented by node 206 a shown in FIG. 2A) that is a child of the first molecular category (e.g., represented by node 204 b) in the hierarchy of molecular categories. In some embodiments, the hierarchy of RNA-based machine learning classifiers further comprises a fourth RNA-based machine learning classifier (e.g., RNA-based machine learning classifier 516 b) corresponding to the fourth molecular category. In some embodiments, the processing further comprises processing the fourth RNA expression data using the fourth RNA-based machine learning classifier (e.g., by processing the fourth RNA expression data to obtain RNA features 516 a with RNA classifier 516 b) to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.

In some embodiments, the RNA expression data further comprises fifth RNA expression data for a fifth set of genes different from the first, second, and fourth sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a fifth molecular category (e.g., represented by node 206 b shown in FIG. 2A) that is another child of the first molecular category (e.g., represented by node 204 b shown in FIG. 2A) in the hierarchy of molecular categories. In some embodiments, the hierarchy of RNA-based machine learning classifiers further comprises a fifth RNA-based machine learning classifier (e.g., RNA-based molecular category 517 b) corresponding to the fifth molecular category. In some embodiments, the processing further comprises processing the fifth RNA expression data using the fifth RNA-based machine learning classifier (e.g., by processing the fifth RNA expression data to obtain RNA features 517 a with RNA classifier 517 b) to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.

In some embodiments, the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category. In some embodiments, the parent molecular category is a breast cancer molecular category, the first molecular category is a basal breast cancer molecular category, and the second molecular category is a non-basal breast cancer molecular category. In some embodiments, the parent molecular category is a category selected from Table 2 (e.g., renal cell carcinoma), and the first and second molecular categories are children of the parent molecular category in the hierarchy of molecular categories shown in FIGS. 7A-1-7B-2 (e.g., non-clear cell carcinoma and clear cell carcinoma show in FIG. 7A-2 ).

In some embodiments, processing the first RNA expression data using the first RNA-based machine learning classifier comprises: obtaining first RNA features (e.g., a gene ranking obtained by ranking the RNA expression levels for genes associated with the first RNA-based ML classifier) from the first RNA expression data, and applying the first RNA-based machine learning classifier to the first RNA features (e.g., processing the first RNA features using the first RNA-based machine learning classifier) to obtain the first output.

In some embodiments, the first RNA expression data comprises first expression levels (e.g., obtained by RNA sequencing) for the first set of genes. The first RNA expression data may be obtained by accessing RNA sequencing data for a patient's genome and identifying and/or selecting, from this large amount of data, RNA sequencing data for the first set of genes. In some embodiments, the RNA sequencing data may comprise millions of sequencing reads, which may be processed by alignment and/or assembly techniques (using any suitable bioinformatics pipeline) to identify RNA expression levels for the first set of genes. In some embodiments, the first RNA expression data may be stored (and/or manipulated in a computer) using at least one data structure having fields storing RNA expression level values.

In some embodiments, obtaining the first RNA features from the first RNA expression data comprises ranking at least some genes in the first set of genes based on the first expression levels (e.g., rank expression levels in ascending or descending order) to obtain a first gene ranking, the first gene ranking including values (e.g., integers) identifying relative ranks of the at least some genes in the gene ranking, wherein the values are different from the first expression levels. For example, genes [A, B, C], having respective expression levels of 0.01, 0.56, and 0.45, would be ranked [1, 3, 2] if they are to be ranked in ascending order. In some embodiments, a gene ranking may be stored (and/or manipulated in a computer) using at least one data structure having fields storing gene ranking values. In some embodiments, applying the first RNA-based machine learning classifier to the first RNA features comprises applying the first RNA-based machine learning classifier to the first gene ranking to obtain the first output (e.g., processing the gene ranking using the first RNA-based machine learning classifier by providing the gene ranking values as inputs to the first RNA-based machine learning classifier).

In some embodiments, processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output comprises processing the first RNA expression data to obtain a first probability (or likelihood or other numerical or categorical value) indicating that the first molecular category is a first candidate molecular category for the biological sample (e.g., relative to the probability that the first molecular category is not a candidate molecular category for the biological sample and/or relative to the probability that the first molecular category is a molecular category for the biological sample). In some embodiments, processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output comprises processing the second RNA expression data to obtain a second probability (or likelihood or other numerical or categorical value) indicating that the second molecular category is a second candidate molecular category for the biological sample.

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold (e.g., a threshold of at least 0.02, at least 0.05, at least 0.1, or at least 0.5), and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold. In some embodiments, identifying the at least one candidate molecular category for the biological sample further comprises excluding the first molecular category from the at least one candidate molecular category identified for the biological sample when the first probability does not exceed the threshold (e.g., the molecular category is not likely a candidate molecular category for the biological sample).

In some embodiments, identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to the second probability (e.g., comparing probabilities output by machine learning classifiers at a same level of the hierarchy of machine learning classifiers), and identifying the first molecular category as a candidate molecular category of the at least one candidate molecular category for the biological sample when the first probability exceeds the second probability (e.g., at a level of the hierarchy, identifying the molecular category associated with the machine learning classifier that output the highest probability).

In some embodiments, the first molecular category is a molecular category selected from molecular categories listed in Table 2. For example, the first molecular category is breast cancer, as selected from Table 2.

In some embodiments, the first set of genes comprises at least 10 genes listed in Table 3 corresponding to the first molecular category. For example, the first set of genes may comprise at least 20 genes, at least 40 genes, at least 60 genes, at least 80 genes, at least 100 genes, at least 150 genes, at least 200 genes, at least 300 genes, between 10 and 300 genes, between 10 and 200 genes, between 10 and 100 genes, between 10 and 80 genes, between 20 and 300 genes, between 20 and 100 genes, between 40 and 300 genes, between 40 and 100 genes, between 50 and 300 genes, or between 50 and 100 genes, in each case being selected from the genes listed in Table 3.

In some embodiments, the hierarchy of RNA-based machine learning classifiers comprises at least 10 RNA-based machine learning classifiers. For example, the hierarchy of RNA-based machine learning classifiers may comprise at least 10 RNA-based machine learning classifiers, at least 20 RNA-based machine learning classifiers, at least 30 RNA-based machine learning classifiers, at least 40 RNA-based machine learning classifiers, at least 50 RNA-based machine learning classifiers, at least 60 RNA-based machine learning classifiers, at least 70 RNA-based machine learning classifiers, at least 80 RNA-based machine learning classifiers, between 10 and 50 machine learning classifiers, between 10 and 100 machine learning classifiers, or any other suitable range within these ranges.

In some embodiments, the first RNA-based machine learning classifier is a gradient-boosted decision tree classifier, a neural network classifier, a logistic regression classifier, a support vector machine classifier, a Bayesian classifier, a random forest classifier, any other type of gradient boosted classifier, or any other suitable type of machine learning classifier. In some embodiments, the first classifier may comprise between 10 and 100 parameters, between 100 and 1000 parameters, between 1000 and 10,000 parameters, between 10,000 and 100,000 parameters or more than 100K parameters. Processing input data with a classifier comprises performing calculations using values of the machine learning classifier parameters and the values of the input to the classifier to obtain the corresponding output. Such calculations may involve hundreds, thousands, tens of thousands, hundreds of thousands or more calculations, in some embodiments.

In some embodiments, each RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is one of a gradient-boosted decision tree classifier, a neural network classifier, a logistic regression classifier, a support vector machine classifier, a Bayesian classifier, a random forest classifier, any other type of gradient boosted classifier, or any other suitable type of machine learning classifier.

In some embodiments, all classifiers in the machine learning classifier hierarchy (whether the hierarchy of RNA-based or DNA-based classifiers) are of a same type (e.g., having different parameters and inputs, but the same architecture, for example, all being gradient boosted decision tree classifiers or all being neural network classifiers). In some embodiments, some of the classifiers in the machine learning classifier hierarchy may be different (e.g., some may be support vector machines and others may be gradient boosted decision tree classifiers).

In some embodiments, the first RNA expression data comprises expression levels for between 20 and 300 genes. For example, the first RNA expression data may comprise expression levels for at least 20 genes, at least 40 genes, at least 60 genes, at least 80 genes, at least 100 genes, at least 150 genes, at least 200 genes, at least 300 genes, between 10 and 300 genes, between 10 and 200 genes, between 10 and 100 genes, between 10 and 80 genes, between 20 and 300 genes, between 20 and 100 genes, between 40 and 300 genes, between 40 and 100 genes, between 50 and 300 genes, or between 50 and 100 genes.

In some embodiments, the hierarchy of machine learning classifiers may include multiple machine learning classifiers, each of which is trained to determine whether to identify a respective molecular category as a candidate molecular category for a biological sample. In some embodiments, the hierarchy of machine learning classifiers include at least 10, at least 20, at least 40, at least 50, at least 60, between 10 and 50, between 25 and 100 machine learning classifiers or any suitable range within these ranges. Thus, in some embodiments, the machine learning classifiers in a hierarchy of machine learning classifiers may be in a one-to-one correspondence with at least some (e.g., all) molecular categories in the hierarchy of molecular categories.

In some embodiments, the computer-implemented techniques for identifying at least one candidate molecular category for a biological sample further involve the use of DNA expression data in addition to (or instead of) the RNA expression data. For example, in some embodiments, the techniques further include obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject (e.g., a patient) and processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers (e.g., hierarchy 550 shown in FIG. 5A-2 ) corresponding to the hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs (e.g., probabilities 565-567 shown in FIG. 5A-2 ). The hierarchy of DNA-based machine learning classifiers is a different hierarchy than the hierarchy of RNA-based machine learning classifiers. For example, the hierarchy of DNA-based machine learning classifiers includes machine learning classifiers trained using DNA expression data (e.g., using features derived from the DNA expression data), while the hierarchy of RNA-based machine learning classifiers includes machine learning classifiers trained using RNA expression data (e.g., using features derived from the RNA expression data). In some embodiments, identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the DNA-based machine learning classifier outputs. For example, by processing the DNA-based machine learning classifier outputs and the RNA-based machine learning classifier outputs using a model or by selecting between the DNA-based machine learning classifier outputs or the RNA-based machine learning classifier outputs. Accordingly, the hierarchy of DNA-based machine learning classifiers may be used together with or instead of the hierarchy of RNA-based machine learning classifiers.

In some embodiments, processing the DNA expression data comprises: obtaining DNA features from the DNA expression data (e.g., by deriving them from the DNA expression data), and applying at least one DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers to at least some of the DNA features (e.g., processing at least some of the DNA features using a classifier of the hierarchy of DNA-based classifiers) to obtain the DNA-based machine learning classifier outputs.

In some embodiments, the DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene (e.g., a mutation in DNAH5, as shown in Table 5). A feature providing such an indication may be a binary feature, whereby one value indicates the presence of the pathogenic mutation and the other value indicates its absence.

In some embodiments, the DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene (e.g., a hotspot mutation in PPP2R1A, as shown in Table 5). A feature providing such an indication may be a binary feature, whereby one value indicates the presence of the hotspot mutation and the other value indicates its absence.

In some embodiments, the DNA features comprise one or more features (e.g., one or more numerical values) indicating tumor mutational burden (e.g., indicative of the number of mutations found in the DNA of cancer cells) for the biological sample.

In some embodiments, the DNA features comprise one or more features (e.g., one or more numerical values) indicating a normalized copy number for each chromosome segment (e.g., a bin, an arm, or a chromosome) of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.

In some embodiments, the DNA features comprise one or more features (e.g., one or more numerical values) indicating loss of heterozygosity (LOH) for each chromosome segment (e.g., a bin, an arm, or a chromosome) of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.

In some embodiments, the DNA features comprise one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes and/or one or more non-protein coding genes. Each such feature may be a binary feature, whereby one value indicates the presence of a protein coding gene and the other value indicates its absence.

In some embodiments, the DNA features comprise one or more features (e.g., one or more binary features) indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene (e.g., with a specific gene, or with any other gene).

In some embodiments, the DNA features comprise one or more features (e.g., one or more numerical values) indicating ploidy (e.g., the number of chromosomes occurring in the nucleus of the cell) for the biological sample.

In some embodiments, the DNA features comprise one or more features (e.g., one or more binary features) indicating whether the DNA expression data indicates presence of microsatellite instability (MSI) (e.g., a condition of hypermutability that results from impaired DNA mismatch repair).

In some embodiments, the DNA features, provided as input to each DNA-based machine learning classifier in the hierarchy, comprise at least ten features listed in Table 5. For example, the DNA features may comprise at least 20 features, at least 40 features, at least 60 features, at least 80 features, at least 100 features, at least 150 features, at least 200 features, at least 300 features, between 10 and 300 features, between 10 and 200 features, between 10 and 100 features, between 10 and 80 features, between 20 and 300 features, between 20 and 100 features, between 40 and 300 features, between 40 and 100 features, between 50 and 300 features, or between 50 and 100 features.

In some embodiments, the identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of the purity of the biological sample. For example, the sample purity may affect the data and therefore impact (e.g., invalidate) the predictions output by one or both of the RNA-based and DNA-based machine learning classifiers. Therefore, one or more outputs may be discarded or considered with greater (or lesser) weight when identifying the at least one candidate molecular category.

In some embodiments, the identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of a site from which the biological sample was obtained. For example, the expression data for the normal tissue from the sample site may be used (e.g., normal lung tissue when the biological sample was obtained from the lung). In some embodiments, at least one machine learning classifier of the hierarchy of RNA-based and DNA-based machine learning classifiers is trained to output an indication of whether the biological sample belongs to the normal tissue.

In some embodiments, the hierarchy of DNA-based machine learning classifiers comprises at least 10 DNA-based machine learning classifiers. For example, the hierarchy of DNA-based machine learning classifiers may comprise at least 10 DNA-based machine learning classifiers, at least 20 DNA-based machine learning classifiers, at least 30 DNA-based machine learning classifiers, at least 40 DNA-based machine learning classifiers, at least 50 DNA-based machine learning classifiers, at least 60 DNA-based machine learning classifiers, at least 70 DNA-based machine learning classifiers, at least 80 DNA-based machine learning classifiers, between 10 and 50 machine learning classifiers, between 10 and 100 machine learning classifiers, or any other suitable range within these ranges.

In some embodiments, the hierarchy of DNA-based machine learning classifiers comprises a first DNA-based machine learning classifier, which is a gradient-boosted decision tree classifier, a neural network classifier, a logistic regression classifier, a support vector machine classifier, a Bayesian classifier, a random forest classifier, any other type of gradient boosted classifier, or any other suitable type of machine learning classifier. In some embodiments, the first DNA based machine learning classifier may comprise between 10 and 100 parameters, between 100 and 1000 parameters, between 1000 and 10,000 parameters, between 10,000 and 100,000 parameters or more than 100K parameters. Processing input data with a classifier comprises performing calculations using values of the machine learning classifier parameters and the values of the input to the classifier to obtain the corresponding output. Such calculations may involve hundreds, thousands, tens of thousands, hundreds of thousands or more calculations, in some embodiments.

In some embodiments, each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is one of a gradient-boosted decision tree classifier, a neural network classifier, a logistic regression classifier, a support vector machine classifier, a Bayesian classifier, a random forest classifier, any other type of gradient boosted classifier, or any other suitable type of machine learning classifier. In some embodiments, all classifiers in the machine learning classifier hierarchy are of a same type (e.g., having different parameters and inputs, but the same architecture, for example, all being gradient boosted decision tree classifiers or all being neural network classifiers). In some embodiments, some of the classifiers in the machine learning classifier hierarchy may be different (e.g., some may be support vector machines and others may be gradient boosted decision tree classifiers).

In some embodiments, the techniques involve using the at least one identified candidate molecular category for a sample obtained from a subject to identify at least one therapy to treat the subject. The identified at least one therapy may then be administered to the subject. A molecular category may be used to identify the at least one therapy by identifying any therapies known to be therapeutically effective for the identified molecular category. For example, when a molecular category is associated with one or more ICD codes, the ICD codes may be used to identify (either automatically by software or manually by a clinician) any therapies known to be therapeutically effective for the identified ICD codes. Where the therapies are identified from one or more molecular categories by software, the identified therapy or therapies may be presented to a clinician (e.g., via a graphical user interface generated by the software or in any other suitable way, as aspects of the technology described herein are not limited in this respect). In some embodiments a molecular category may encompass or correspond to a plurality of ICD codes (e.g., 2, 3, 4, 5, . . . ) and that one or more recommended therapies for any one or more of them could be identified (e.g., selected by a clinician, recommended to a clinician) for treatment. The identified therapy or therapies may then be administered to the patient.

In some embodiments, the techniques further include generating, using the hierarchy of molecular categories, a graphical user interface (GUI) (e.g., the screenshot shown in FIG. 1B) including a visualization (e.g., a graph including nodes and edges) indicating the at least one molecular category identified for the biological sample.

In some embodiments, the techniques further include: receiving an indication of a clinical diagnosis of the biological sample (e.g., from a clinician or researcher who analyzed the biological sample) and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample. For example, the techniques described herein may be used to confirm or correct a diagnosis made by a clinician and/or to perform other types of quality control.

In some embodiments, the first molecular category of the hierarchy of molecular categories is one of a neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma.

In some embodiments, the first molecular category of the hierarchy of molecular categories is associated with one or more ICD codes. In some embodiments, the first molecular category of the hierarchy of molecular codes is associated with a histological subtype of a cancer.

Some embodiments provide for computer-implemented techniques for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: (a) obtaining DNA expression data previously obtained by processing (e.g., sequencing) the biological sample obtained from the subject; (b) processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers (e.g., the hierarchy of DNA-based machine learning classifiers 550 shown in FIG. 5A-2 ) corresponding to a hierarchy of molecular categories (e.g., the hierarchy of molecular categories 200 shown in FIG. 2A) to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category (e.g., represented by node 202 shown in FIG. 2A) and first and second molecular categories (e.g., represented by nodes 204 b and 204 a of FIG. 2A) that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers (e.g., classifiers 553 b and 554 b shown in FIG. 5A-2 ) corresponding to the first and second molecular categories, the processing comprising: (i) processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output (e.g., a probability, a likelihood, or other numerical or categorical value) indicative of whether the first molecular category is a candidate molecular category for the biological sample; (ii) processing the second DNA expression data (e.g., using the second DNA-based machine learning classifier to obtain the second output (e.g., a probability, a likelihood, or other numerical or categorical value) indicative of whether the second molecular category is a candidate molecular category for the biological sample; and (c) identifying, using at least some of the DNA-based machine learning classifier outputs (e.g., probabilities 565, 566, and 567 shown in FIG. 5A-2 ) including the first output and the second output, at least one candidate molecular category (e.g., one more candidate molecular categories corresponding to one or more levels of the hierarchy of DNA-based machine learning classifiers) for the biological sample.

In some embodiments, the DNA expression data further comprises third DNA expression data for a third set of genes different from the first and second sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a third molecular category (e.g., represented by node 204 c) that is a child of the parent molecular category in the hierarchy of molecular categories. In some embodiments, the hierarchy of DNA-based machine learning classifiers further comprises a third DNA-based machine learning classifier (e.g., DNA-based machine learning classifier 555 b) corresponding to the third molecular category. In some embodiments, the processing further comprises processing the third DNA expression data using the third DNA-based machine learning classifier (e.g., by processing DNA features 555 a, obtained from the third DNA expression data, with DNA-based machine learning classifier 555 b) to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the third output.

In some embodiments, the DNA expression data further comprises fourth DNA expression data for a fourth set of genes different from the first and second sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a fourth molecular category (e.g., represented by node 206 a shown in FIG. 2A) that is a child of the first molecular category (e.g., represented by node 204 b) in the hierarchy of molecular categories. In some embodiments, the hierarchy of DNA-based machine learning classifiers further comprises a fourth DNA-based machine learning classifier (e.g., DNA-based machine learning classifier 556 b) corresponding to the fourth molecular category. In some embodiments, the processing further comprises processing the fourth DNA expression data using the fourth DNA-based machine learning classifier (e.g., by processing DNA features 556 a, obtained using the fourth DNA expression data, with DNA-based machine learning classifier 556 b) to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.

In some embodiments, the DNA expression data further comprises fifth DNA expression data for a fifth set of genes different from the first, second, and fourth sets of genes. In some embodiments, the hierarchy of molecular categories further comprises a fifth molecular category (e.g., represented by node 206 b shown in FIG. 2A) that is a child of the first molecular category (e.g., represented by node 204 b shown in FIG. 2A) in the hierarchy of molecular categories. In some embodiments, the hierarchy of DNA-based machine learning classifiers further comprises a fifth DNA-based machine learning classifier (e.g., DNA-based machine learning classifier 557 b) corresponding to the fifth molecular category. In some embodiments, the processing further comprises processing the fifth DNA expression data using the fifth DNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.

Molecular Categories

As described above, a “molecular category” refers to a category or group of biological samples (e.g., tumor samples) that have similar molecular features (e.g., features derived from expression data). In some embodiments, a molecular category may be associated with one or more clinical diagnoses. For example, in some embodiments, a molecular category may be associated with one or more International Classification of Diseases (ICD) codes. Examples are provided in Table 1. In some embodiments, a molecular category may be associated with a histological subtype of a cancer. For example, non-basal breast cancer and basal breast cancer are molecular categories, shown in FIG. 7A-2 , which are associated with histological subtypes of breast cancer. Other examples are provided herein.

In some embodiments, a molecular category may correspond to a known cancer subtype, for a known histological cancer cell or cancer tissue subtype. However, in other embodiments, a molecular category may be a newly identified category that is clinically relevant and useful for diagnostic, prognostic, and/or therapeutic purposes.

As described herein, molecular categories may be organized into a hierarchy of molecular categories in which molecular categories at different levels of the hierarchy have differing degrees of specificity—molecular categories at higher levels of the hierarchy are broader categories having lower specificity, while molecular categories at lower levels of the hierarchy are narrower categories having higher specificity. In some embodiments, a hierarchy of molecular categories (e.g., hierarchy 200 shown in FIG. 2A) includes nodes, each of which represents a respective molecular category, and edges, which define the hierarchical (e.g., parent-child) relationships between the molecular categories. A parent node (e.g., node 204 b shown in FIG. 2A) in the hierarchy is a node that is connected by edges to one or more child nodes (e.g., nodes 206 a-b shown in FIG. 2A). In some embodiments, a parent node represents a molecular category that can be subdivided into more specific molecular categories, which are represented by the child nodes of the parent nodes.

In some embodiments, nodes at different levels of the hierarchy represent molecular categories that have differing degrees of specificity. In some embodiments, a node falling within the upper level(s) of the hierarchy represents a relatively general molecular category, meaning that the molecular category encompasses a broad range of molecular features shared by biological samples associated with multiple different diagnoses associated with multiple different locations in the body. As an example, such a molecular category may encompass molecular features of biological samples that are associated with glioma, testicular germ cell tumor, adenocarcinoma, squamous cell carcinoma, neuroendocrine tumor, mesothelioma, sarcoma, and melanoma. In some embodiments, a node falling within the middle level(s) of the hierarchy represents a molecular category that encompasses molecular features associated with a non-heterogeneous type of cancer. For example, such a molecular category may encompass molecular features of a biological sample associated with ovarian cancer. In some embodiments, a node falling within the bottom level(s) of the hierarchy represents a relatively specific molecular category, meaning that the molecular category encompasses a narrow range of molecular features shared by biological samples associated with a particular histological subtype of cancer (e.g., a molecularly-defined type of cancer). For example, such a molecular category may encompass molecular features of biological samples that are associated with non-basal breast cancer, which is a histological subtype of breast cancer.

Numerous examples of such hierarchies and their constituent molecular categories are provided herein including with reference to FIGS. 1B, 2A 7A-1-7A-3, and 7B-1-7B-5.

TABLE 1 List of ICD codes of disease(s) associated with the molecular categories Molecular category ICD Code Neoplasm C80 Solid Neoplasm C76 Hematologic Neoplasm C96 Melanoma C43 Sarcoma C92.3, C47, C48, C47.0-C47.6, C-47.8-C48.2, C48.8, C49, C49.0-49.6, C49.8, C49.9, C22.3, C22.4, C54.2 Mesothelioma C45 Neuroendocrine C7A, C7A.0, C7B, C25.4 Squamous Cell Carcinoma Adenocarcinoma Glioma C71.9 Testicular Germ Cell Tumor C62 Pheochromocytoma C74.1 Cervical Squamous Cell C53, C54.9 Carcinoma Liver Neoplasm C22, C24 Lung Adenocarcinoma C34 High Grade Glioma IDH Mut C71.9 Thyroid Neoplasm C73 Squamous Cell Lung Carcinoma C34 Thymoma C37 Prostate Adenocarcinoma C61 Urinary Bladder Urothelial C67 Carcinoma Oligodendroglioma C71.9 Squamous Cell Carcinoma of the C12, C13, C11, C10 Head and Neck Gastrointestinal Adenocarcinoma C15-C20 Gynecological Renal Cell Carcinoma C64 Astrocytoma C71.9 Pancreatic Adenocarcinoma C25 Stomach Adenocarcinoma C16, C16.9 Pancreatic Adenocarcinoma C25 Breast Cancer C50 Ovarian Cancer C56, C57.0 Uterine Corpus Endometrial C53, C54, C54.1, C55 Carcinoma Non-Clear Cell Carcinoma C64 Clear Cell Carcinoma C64 Basal Breast Cancer C50 Non-Basal Breast Cancer C50

Following below are more detailed descriptions of various concepts related to, and embodiments of, the systems and methods developed by the inventors for identifying a candidate molecular category for a biological sample. It should be appreciated that various aspects described herein may be implemented in any of numerous ways. Examples of specific implementations are provided herein for illustrative purposes only. In addition, the various aspects described in the embodiments below may be used alone or in any combination and are not limited to the combinations explicitly described herein.

FIG. 1A depicts an illustrative technique 100 for identifying a candidate molecular category 105 for a biological sample 101 based on expression data 103 obtained using a sequencing platform 102 to process biological sample 101. The candidate molecular category 105 is identified by processing the expression data 103 using computing device 104.

In some embodiments, the illustrated technique 100 may be implemented in a clinical or laboratory setting. For example, the illustrated technique 100 may be implemented on a computing device 104 that is located within the clinical or laboratory setting. In some embodiments, the computing device 104 may directly obtain the expression data 103 from a sequencing platform 102 located within the clinical or laboratory setting. For example, a computing device 104 included in the sequencing platform 102 may directly obtain the expression data 103 from the sequencing platform 102. In some embodiments, the computing device 104 may indirectly obtain expression data 103 from a sequencing platform 102 that is located within or external to the clinical or laboratory setting. For example, a computing device 104 that is located within the clinical or laboratory setting may obtain expression data 103 via a communication network, such as Internet or any other suitable network, as aspects of the technology described herein are not limited to any particular communication network.

Additionally or alternatively, the illustrated technique 100 may be implemented in a setting that is remote from a clinical or laboratory setting. For example, the illustrated technique 100 may be implemented on a computing device 104 that is located externally from a clinical or laboratory setting. In this case, the computing device 104 may indirectly expression data 103 that is generated using a sequencing platform 102 located within or external to a clinical or laboratory setting. For example, the expression data 103 may be provided to computing device 104 via a communication network, such as Internet or any other suitable network, as aspects of the technology described herein are not limited to any particular communication network.

As shown in FIG. 1A, the technique 100 involves processing a biological sample 101 using a sequencing platform 102, which produces expression data 103. The biological sample 101 may be obtained from a subject having, suspected of having, or at risk of having cancer or any immune-related diseases. The biological sample 101 may be obtained by performing a biopsy or by obtaining a blood sample, a salivary sample, or any other suitable biological sample from the subject. The biological sample 101 may include diseased tissue (e.g., cancerous), and/or healthy tissue. In some embodiments, the origin or preparation methods of the biological sample may include any of the embodiments described herein including with respect to the “Biological Samples” section.

In some embodiments, the sequencing platform 102 may be a next generation sequencing platform (e.g., Illumina™, Roche™, Ion Torrent™, etc.), or any high-throughput or massively parallel sequencing platform. In some embodiments, the sequencing platform 102 may include any suitable sequencing device and/or any sequencing system including one or more devices. In some embodiments, these methods may be automated, in some embodiments, there may be manual intervention. In some embodiments, the expression data 103 may be the result of non-next generation sequencing (e.g., Sanger sequencing, microarrays).

Expression data 103 can include the sequence data generated by a sequencing protocol (e.g., the series of nucleotides in a nucleic acid molecule identified by next-generation sequencing, sanger sequencing, etc.) as well as information contained therein (e.g., information indicative of source, tissue type, etc.) which may also be considered information that can be inferred or determined from the sequence data. In some embodiments, expression data 103 can include information included in a FASTA file, a description and/or quality scores included in a FASTQ file, an aligned position included in a BAM file, and/or any other suitable information obtained from any suitable file.

In some embodiments, the expression data 103 may be generated using a nucleic acid from a sample from a subject. Reference to a nucleic acid may refer to one or more nucleic acid molecules (e.g., a plurality of nucleic acid molecules). In some embodiments, the sequence information may be sequence data indicating a nucleotide sequence of deoxyribonucleic acid (DNA) and/or ribonucleic acid (RNA) from a previously obtained biological sample of a subject having, suspected of having, or at risk of having a disease.

In some embodiments, the nucleic acid is RNA. In some embodiments, sequenced RNA comprises both coding and non-coding transcribed RNA found in a sample. When such RNA is used for sequencing the sequencing is said to be generated from “total RNA” and also can be referred to as whole transcriptome sequencing. Alternatively, the nucleic acids can be prepared such that the coding RNA (e.g., mRNA) is isolated and used for sequencing. This can be done through any means known in the art, for example by isolating or screening the RNA for polyadenylated sequences. This is sometimes referred to as mRNA-Seq.

In some embodiments, the nucleic acid is DNA. In some embodiments, the nucleic acid is prepared such that the whole genome is present in the nucleic acid. In some embodiments, the nucleic acid is processed such that only the protein coding regions of the genome remain (e.g., the exome). When nucleic acids are prepared such that only the exome is sequenced, it is referred to as whole exome sequencing (WES). A variety of methods are known in the art to isolate the exome for sequencing, for example, solution-based isolation wherein tagged probes are used to hybridize the targeted regions (e.g., exons) which can then be further separated from the other regions (e.g., unbound oligonucleotides). These tagged fragments can then be prepared and sequenced.

In some embodiments, expression data 103 may include raw DNA or RNA sequence data, DNA exome sequence data (e.g., from whole exome sequencing (WES), DNA genome sequence data (e.g., from whole genome sequencing (WGS)), RNA expression data, gene expression data, bias-corrected gene expression data, or any other suitable type of sequence data comprising data obtained from the sequencing platform 102 and/or comprising data derived from data obtained from sequencing platform 102. In some embodiments, the origin or preparation of the expression data 103 may include any of the embodiments described with respect to the “Expression Data” and “Obtaining RNA expression data” sections.

Regardless of the expression data 103 obtained, the expression data 103 is processed using 104. In some embodiments, computing device 104 can be one or multiple computing devices of any suitable type. For example, the computing device 104 may be a portable computing device (e.g., a laptop, a smartphone) or a fixed computing device (e.g., a desktop computer, a server). When computing device 104 includes multiple computing devices, the device(s) may be physically co-located (e.g., in a single room) or distributed across multiple physical locations. In some embodiments, the computing device 104 may be part of a cloud computing infrastructure. In some embodiments, one or more computer(s) 104 may be co-located in a facility operated by an entity (e.g., a hospital, a research institution). In some embodiments, the one or more computing device(s) 104 may be physically co-located with a medical device, such as a sequencing platform 102. For example, a sequencing platform 102 may include computing device 104. FIG. 3 system 300 including example computing device 304 and software 310

In some embodiments, the computing device 104 may be operated by a user such as a doctor, clinician, researcher, patient, or other individual. For example, the user may provide the expression data 103 as input to the computing device 104 (e.g., by uploading a file), and/or may provide user input specifying processing or other methods to be performed using the expression data 103.

In some embodiments, expression data 103 may be processed by one or more software programs running on computing device 104 (e.g., as described herein including at least with respect to FIG. 3 ). In particular, in some embodiments, the expression data 103 may be processed by a hierarchy of machine learning classifiers that corresponds to a hierarchy of molecular categories. For example, a first machine learning classifier of the hierarchy of machine learning classifiers may be used to process first expression data associated with a first molecular category. The first machine learning classifier may be trained to predict whether the biological sample 101 belongs to the first molecular category in the hierarchy of molecular categories. In some embodiments, such processing may be performed for some, most, or all of the molecular categories included in the hierarchy of molecular categories to obtain machine learning classifier outputs. Illustrative techniques for processing the expression data are described herein including at least with respect to FIG. 2B and FIGS. 4A-4C.

Based on the outputs of the machine learning classifiers, including the output of the first machine learning classifier, in some embodiments, at least one candidate molecular category 105 are identified for the biological sample 101. The at least one candidate molecular category 105 may include one or multiple candidate molecular categories for the biological sample 101. In some embodiments, candidate molecular categories 105 include molecular categories at different levels of the hierarchy of molecular categories. For example, a parent node representing a broad molecular category and one of its child nodes representing a more specific molecular category may be identified for the biological sample. Additionally or alternatively, multiple nodes representing multiple molecular categories at the same level of the hierarchy may be identified for the biological sample. In some embodiments, no candidate molecular categories may be identified for the biological sample.

In some embodiments, the at least one identified candidate molecular category 105 may be provided as output. In some embodiments, for example, the identified candidate molecular categories may be used to generate a report to be output to user (e.g., via a graphical user interface (GUI). FIG. 1B is a screenshot of an example report indicating candidate molecular categories identified using illustrative technique 100. As shown, the example report provides a visualization of the hierarchy of molecular categories. The report 180 also indicates the probability that the biological sample belongs to each particular molecular category, the type of expression data used for candidate molecular category identification, and different features associated with the identified molecular category (e.g., most probable molecular categories). For example, as shown in FIG. 1D, features associated with the identified molecular category include mutations, fusions, and expression of particular genes.

In some embodiments, the at least one candidate molecular category 105 may be used to identify a tumor-specific treatment for the subject from which the biological sample 101 was obtained. For example, as described above, a molecular category may be associated with at least one clinical diagnosis. A treatment known to be effective for tumors of the clinical diagnosis may be identified to treat the biological sample 101.

Additionally or alternatively, the at least one candidate molecular diagnosis may be used to confirm a clinical diagnosis that was previously identified for the biological sample 101. For example, a clinical diagnosis may be received from a clinician. The illustrative techniques 100 may include comparing the clinical diagnosis received from the clinician to the clinical diagnosis associated with at least one candidate molecular category 105 identified for the biological sample 101. If the diagnoses do not match, then the clinical diagnoses associated with the at least one candidate molecular category 105 may be provided to the clinician to inform treatment selection.

Hierarchy of Molecular Categories

In some embodiments, the techniques described herein include using a hierarchy of molecular categories to identify candidate molecular categories for a biological sample. An illustrative hierarchy 230 is shown in FIG. 2A.

In some embodiments, the hierarchy 230 of molecular categories is a directed graph that includes nodes and edges. In some embodiments, a node represents a molecular category, while an edge represents a relationship between molecular categories. For example, as shown in FIG. 2A, node 202 represents molecular category A and node 204 a represents molecular category B. The edge between node 202 and node 204 a represents a relationship between those nodes. In particular, node 202 is a parent node of child node 204 a. Similarly, node 202 is a parent node of nodes 204 b-c, and node 204 b is a parent node of child nodes 206 a-b. It should be appreciated, however, that the hierarchy of molecular categories 200 is not restricted to the nodes shown in FIG. 2A. Rather, any suitable number of nodes representing any suitable number of molecular categories may be included in the hierarchy of molecular categories. For example, the hierarchy of molecular categories 200 may include at least 10 nodes representing 10 molecular categories, at least 20 nodes representing 20 molecular categories, at least 40 nodes representing 40 molecular categories, at least 60 nodes representing 60 molecular categories, or at least 100 nodes representing 100 molecular categories. Additional example hierarchies of molecular categories are provided at least in FIG. 1B, in FIGS. 7A-1-7A-3 , and in FIGS. 7B-1-7B-5 .

In some embodiments, as described above, node at higher levels of the hierarchy represent molecular categories that are more general, meaning that they encompass a broad range of molecular features shared by biological samples associated with multiple different diagnoses associated with multiple different locations in the body. For example, molecular category A may be a general molecular category, such as neoplasm (e.g., as shown in FIGS. 7A-1-7A-3 and 7B-1-7B-5 ), which is general molecular category associated with multiple different diagnoses associated with multiple different locations in the body.

In some embodiments, a node falling within the middle levels of the hierarchy represents a molecular category that encompasses molecular features associated with a non-heterogeneous type of cancer. For example, molecular category C, represented by node 204 b, may represent a molecular category such as ovarian cancer or thymoma (e.g., as shown in FIGS. 7A-1-7A-3 and 7B-1-7B-5 ), each of which encompasses molecular features associated with a respective non-heterogeneous type of cancer.

In some embodiments, molecular categories at lower levels of the hierarchy of molecular categories are more specific such that they encompass a narrow range of molecular features shared by biological samples associated with a particular histological subtype of cancer (e.g., a molecularly-defined type of cancer). For example, node 206 b represents a molecular category F at the bottom level of the example hierarchy of molecular classifiers 200. Molecular category F may include, for example, basal breast cancer or non-basal breast cancer (e.g., as shown in FIGS. 7A-1-7A-3 and 7B-1-7B-5 ), each of which is associated with a molecularly-defined type of cancer.

Identifying Candidate Molecular Categories

FIG. 2B-1 is a diagram depicting an illustrative technique 220 for processing expression data to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein. In some embodiments, illustrative technique 220 includes processing expression data 221 to obtain features 222 and apply machine learning techniques 230 to the features 222 to identify at least one candidate molecular category 229 for the biological sample from which the expression data was obtained.

In some embodiments, the expression data 221 may include any suitable expression data, such as the expression data described above with respect to FIG. 1A and described herein including in the section “Expression Data.” For example, the expression data 221 may include RNA expression data and/or DNA expression data.

In some embodiments, expression data 221 is processed to obtain features 222 from the expression data 221. In some embodiments, processing the expression data 221 includes generating numeric and/or binary data based on the expression data to obtain the features 222. For example, when the expression data 221 is RNA expression data, processing the expression data 221 may include ranking expression levels of genes in one or more gene sets. Additionally or alternatively, when the expression data 221 is DNA expression data, processing the expression data 221 may include detecting determining copy numbers of genes, detecting the presence or absence of gene mutations, detecting the presence or absence of mutational hotspots, detecting the presence or absence of gene fusion, quantifying copy number alterations, quantifying loss of heterozygosity, quantifying tumor mutational burden, determining ploidy, and/or detecting microsatellite instability (MSI) status. Example RNA and DNA features are described herein in more detail including with respect to FIGS. 6A and 6B.

In some embodiments, features 222 include subsets of features that are each associated with a particular molecular category. For examples, features B 224 a, features C 225 a, features D 226 a, features E 227 a, and features F 228 a are associated, respectively, with molecular category B 224 c, molecular category C 225 c, molecular category D 226 c, molecular category E 227 c, and molecular category F 228 c.

In some embodiments, a subset of RNA features includes a ranked gene set, where genes in the gene set are specific to the associated molecular category. For example, features E 227 a may include a ranked set of genes, where genes in the gene set are specific to molecular category E 227 c. Table 3 lists example genes that are specific to example molecular categories. Techniques for identifying genes that are specific to a molecular category are described herein including at least with respect to FIG. 8A.

Additionally or alternatively, a subset of DNA features includes DNA features (e.g., mutational burden, ploidy, and other the features described with respect to FIG. 6B) that are specific to the molecular category. For example, features D 226 may include DNA features that are specific to molecular category E. Table 5 lists example DNA features that are specific to example molecular categories. Techniques for identifying DNA features that are specific to a molecular category are described herein including at least with respect to FIG. 8B.

In some embodiments, the machine learning techniques 230 include processing features 222 using a hierarchy of machine learning classifiers. As shown, the hierarchy of machine learning classifiers includes machine learning classifier B 224 b, machine learning classifier C 225 b, machine learning classifier D 226 b, machine learning classifier E 227 b, and machine learning classifier F 228 b. Each machine learning classifier may include any suitable classifier and an illustrative example of such a classifier is described herein including at least with respect to FIG. 2C.

In some embodiments, each of the machine learning classifiers corresponds to a molecular category of a hierarchy of molecular categories (e.g., hierarchy 200 of FIG. 2A), meaning that it is trained to process features associated with the molecular category to determine whether to identify the molecular category as a candidate molecular category for the biological sample. For example, machine learning classifier B 224 c is trained to process features B 224 a to determine whether to identify molecular category B 224 c as a candidate molecular category for the biological sample. Techniques for training machine learning classifiers are described herein including at least with respect to FIGS. 8A-8B.

In some embodiments, at least one candidate molecular category 229 is identified as a result of machine learning techniques 230. In some embodiments, the at least one candidate molecular category includes one or multiple of the molecular categories B-F. For example, a candidate molecular category may be identified at each level of the hierarchy of machine learning classifiers. Additionally or alternatively, multiple candidate molecular categories may be identified at one or more levels of the hierarchy. Additionally or alternatively, no candidate molecular category may be identified for one or more levels of the hierarchy.

FIG. 2B-2 is a diagram depicting an example 230 of illustrative technique 250 for processing expression data to identify a candidate molecular category for a biological sample, according to some embodiments of the technology described herein. As explained above, the machine learning techniques 230 are used to process features 222 obtained from expression data 221 to identify candidate molecular categories 229 for the biological sample.

In some embodiments, the machine learning techniques 230 include determining whether to identify any of the molecular categories (e.g., B, C, and D) descending from molecular category A 223 as a candidate molecular category for the biological sample. In some embodiments, the techniques include processing features B 224 a using machine learning classifier B 224 b to determine whether to identify the molecular category B 224 c as a candidate molecular category, processing features C 225 a using machine learning classifier C 225 b to determine whether to identify the molecular category C 225 c as a candidate molecular category, and processing features D 226 a using machine learning classifier D 226 b to determine whether to identify the molecular category D 226 c as a candidate molecular category.

In some embodiments, the machine learning techniques 230 include determining whether to identify any of the molecular categories (e.g., E and F) descending from molecular category C 225 c as a candidate molecular category for the biological sample. In some embodiments, the techniques include processing features E 227 a using machine learning classifier E 227 b to determine whether to identify the molecular category E 227 c as a candidate molecular category and processing features F 228 a using machine learning classifier F 228 b to determine whether to identify the molecular category C 228 c as a candidate molecular category.

In some embodiments, the output of each machine learning classifier is indicative of the probability that biological sample belongs to the particular molecular category corresponding to the machine learning classifier. For example, the output of machine learning classifier B 224 b may indicate the probability that the biological sample belongs to molecular category B 224 c. Techniques for processing features using a machine learning classifier are described herein including at least with respect to FIG. 2C.

In some embodiments, after processing the features 222 using the machine learning classifiers included in the hierarchy of machine learning classifiers, the techniques include identifying candidate molecular categories 229 for the biological sample. In the example shown in FIG. 2B-2 , molecular category C 225 c and molecular category F 228 c are identified as candidate molecular categories 229 for the biological sample. Techniques for identifying candidate molecular categories for the biological sample are described herein including at least with respect to FIG. 2D.

Machine Learning Classifier

As described above, a hierarchy of machine learning classifiers includes multiple machine learning classifiers used to process features obtained from expression data obtained from the biological sample. FIG. 2C shows an illustrative diagram 250 of a two-class classifier, optionally a multi-class classifier, according to some embodiments of the technology described herein.

In some embodiments, a machine learning classifier included in the hierarchy of machine learning classifiers (e.g., machine learning classifier B 224 b) can include for example, a gradient boosted tree, a neural network, a logistic regression model, a support vector machine, a Bayesian classifier, a random forest classifier, or any suitable type of machine learning classifier, as aspects of the technology described herein are not limited to any particular type of machine learning classifier

In some embodiments, the machine learning classifier B 224 b is trained to distinguish between two classes: molecular category B 224 c and not molecular category B 256 a (e.g., all other molecular categories, not including molecular category B 224 c). In particular, the machine learning classifier may be trained to predict the probability B 254 b that the biological sample belongs to molecular category B 224 c versus the probability 256 b that the biological sample does not belong to molecular category B 256 a.

In some embodiments, the machine learning classifier B 224 b is trained to distinguish between biological samples belonging to molecular category B 224 c and not molecular category A 376 b based on features B 224 b obtained from expression data B 251. As explained above with respect to FIG. 2B-1 , in some embodiments, the feature B 224 b are unique to molecular category B 224 b. By processing features B 224 b that are unique to molecular category B 224 c, it is possible for the machine learning classifier B 224 b to distinguish between molecular category B 224 c and not molecular category B 256 a.

In some embodiments, the sample site from which the biological sample was obtained may affect the accuracy of the results of the machine learning classifier B 224 b when the machine learning classifier B 224 b is used to process RNA expression data. Consider, as an example, a tumor sample obtained from the liver that contains normal liver tissue. Since liver neoplasm originates in the liver, the normal tissue from the liver and tumor tissue belonging to the liver neoplasm molecular category may share similar RNA expression profiles. Therefore, a machine learning classifier that receives the tumor sample and is not trained to distinguish between tissue belonging to the liver neoplasm molecular category and normal liver tissue may inaccurately predict a high probability for the liver neoplasm molecular category, even when that is not the case.

To mitigate these biases, in some embodiments, the machine learning classifier B 224 b may comprise a multi-class classifier trained to distinguish between three classes: normal tissue 258 a (e.g., tissue from the sample site that is not diseased), molecular category B 224 c, and molecular category B 254 a. In this embodiment, the machine learning classifier B 224 b may be trained to determine probability 356 b that the biological sample belongs to the normal tissue corresponding to the molecular category B.

FIG. 2D illustrates identifying a candidate molecular category for a biological sample using machine learning classifiers at a same level of a hierarchy of machine learning classifiers, according to some embodiments of the technology described herein.

In some embodiments, classifier B 224 b, classifier C 225 b, and classifier D 226 b are each associated with molecular categories represented by nodes that descend from parent node 223, representing molecular category A. Therefore, classifiers B-D are positioned at a same level (e.g., level N) of the hierarchy of machine learning classifiers as one another.

As described above, the machine learning techniques 230, shown in FIG. 2A, include using a hierarchy of machine learning classifiers to obtain outputs indicating the probability that a biological sample belongs to each molecular category. For example, as shown in FIG. 2D, classifier B 224 a outputs probability B 274 c, classifier C 225 a outputs probability C 275 c, and classifier D 226 b outputs probability D 276 c.

In some embodiments, the probabilities 274 c, 275 c, and 276 c may be used to identify at least one candidate molecular category for the biological sample that corresponds to level N. In some embodiments, the techniques include comparing each of probability B 274 c, probability C 275 c, and probability D 276 c to a threshold. If the probability exceeds the threshold the molecular category may be identified as a candidate molecular category for the biological sample. By contrast, a molecular category corresponding to a classier that output a probability that is below a threshold may be excluded. For example, if the probability exceeds a threshold of at least 0.01, at least 0.05, at least 0.1, at least 0.3, at least 0.5, or at least 0.7 then the molecular category may be identified as a candidate molecular category for the biological sample.

Additionally or alternatively, the probabilities 274 c, 275 c, and 276 c may be compared to one another to identify at least one candidate molecular category for the biological sample. For example, the molecular category or categories corresponding to the highest probability or N highest probabilities at the level of the hierarchy (e.g., level N of the hierarchy) may be identified as a candidate molecular category for the biological sample.

In some embodiments, the techniques 270 are used to identify candidate molecular categories at one or more levels of the hierarchy of machine learning classifiers. For example, the techniques may be used to identify candidate molecular categories at one or multiple levels of the hierarchy.

FIG. 3 is a block diagram of a system 300 including example computing device 304 and software 310, according to some embodiments of the technology described herein.

In some embodiments, computing device 304 includes software 310 configured to perform various functions with respect to the expression data 303. In some embodiments, software 310 includes a plurality of modules. A module may include processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform the function(s) of the module. Such modules are sometimes referred to herein as “software modules.” each of which includes processor executable instructions configured to perform one or more processes, such as the processes described herein including at least with respect to FIGS. 4A-4C and FIGS. 8A-8B.

For example, as shown in FIG. 3 , software 310 includes one or more software modules for processing expression data 303, such as a molecular category identification module 360 and a report generation module 362. In some embodiments, the software 310 additionally includes a user interface module 358, a sequencing platform interface module 348, and/or a data store interface module 342 for obtaining data (e.g., user input, expression data, machine learning classifier(s)). In some embodiments, data is obtained from sequencing platform 344, expression data store 346, and/or machine learning classifier data store 354. In some embodiments, the software 310 further includes machine learning classifier training module 352 for training one or more machine learning classifiers (e.g., stored in machine learning classifier data store 354).

In some embodiments, the molecular category identification module 360 obtains expression data from the expression data store 346 and/or the sequencing platform 344 and obtains machine learning classifiers from the machine learning classifier data store 354.

In some embodiments, the obtained machine learning classifiers include machine learning classifiers that are arranged into one or more hierarchies of machine learning classifiers. In some embodiments, different hierarchies include classifiers trained on different types of data. For example, a hierarchy of RNA-based machine learning classifiers includes classifiers trained using RNA data, while a hierarchy of DNA-based machine learning classifiers includes classifiers trained using DNA data. Regardless of the differences in training data, both hierarchies may be used by the molecular category identification module 360 for the same purpose, as described herein.

In some embodiments, the molecular category identification module 360 processes the obtained expression data using the machine learning classifiers of a first hierarchy of machine learning classifiers (e.g., a hierarchy of RNA-based machine learning classifiers) to identify candidate molecular categories for the biological sample from which the expression data was obtained. For example, the molecular category identification module 360 may process the obtained expression data using machine learning classifiers at a first level of the hierarchy to identify a first candidate molecular category for the biological sample. In some embodiments, the molecular category identification module 360 may process the obtained expression data using machine learning classifiers at a second level of the hierarchy to identify a second candidate molecular category for the biological sample. In some embodiments, the second candidate molecular category may be more specific than the first candidate molecular category. Techniques for using a hierarchy of machine learning classifiers to identify candidate molecular categories for a biological sample are described herein including at least with respect to FIGS. 4A-C.

Additionally or alternatively, the machine learning molecular category identification module 360 processes the obtained expression data using machine learning classifiers of a second hierarchy of machine learning classifiers (e.g., a hierarchy of DNA-based machine learning classifiers) to identify candidate molecular categories for the biological sample. In some embodiments, the results may be used to confirm or take the place the results obtained from the first hierarchy of classifiers.

In some embodiments, the molecular category identification module 360 obtains the expression data and/or the machine learning classifiers via one or more interface modules. In some embodiments, the interface modules include sequencing platform interface module 348 and data store interface module 342. The sequencing platform interface module 348 may be configured to obtain (either pull or be provided) expression data from the sequencing platform 344. The data store interface module may be configured to obtain (either pull or be provided) expression data and/or machine learning classifiers from the expression data store 346 and/or the machine learning classifier data store 354, respectively. The data and/or the machine learning classifiers may be provided via a communication network (not shown), such as Internet or any other suitable network, as aspects of the technology described herein are not limited to any particular communication network.

In some embodiments, expression data store 346 includes any suitable data store, such as a flat file, a data store, a multi-file, or data storage of any suitable type, as aspects of the technology described herein are not limited to any particular type of data store. The expression data store 346 may be part of software 304 (not shown) or excluded from software 304, as shown in FIG. 3 .

In some embodiments, expression data store 346 stores expression data obtained from biological sample(s) of one or more subjects. In some embodiments, the expression data may be obtained from sequencing platform 344 and/or from one or more public data stores and/or studies. In some embodiments, a portion of the expression data may be processed by the molecular category identification module 360 to identify candidate molecular categories for a biological sample. In some embodiments, a portion of the expression data may be used to train one or more machine learning classifiers (e.g., with the machine learning classifier training module 364).

In some embodiments, machine learning classifier data store 354 includes any suitable data store, such as a flat file, a data store, a multi-file, or data storage of any suitable type, as aspects of the technology described herein are not limited to any particular type of data store. The machine learning classifier data store 354 may be part of software 304 (not shown) or excluded from software 310, as shown in FIG. 3 .

In some embodiments, machine learning classifier data store 354 stores one or more hierarchies of machine learning classifiers used to identify candidate molecular categories for a biological sample. In some embodiments, each hierarchy of machine learning classifiers corresponds to a hierarchy of molecular categories. The relationships between the machine learning classifiers in each hierarchy may be stored in the machine learning classifier data store 354. For example, the machine learning classifier data store 354 may store a relationship between a machine learning classifier trained to determine the probably that the biological sample belongs to a molecular category represented by a parent node and a machine learning classifier trained to determine whether the biological sample belongs to a molecular category represented by a child node of the parent node.

In some embodiments, report identification module 362 processes results obtained from the molecular category identification module 360 to generate one or more reports. An example report is described above including at least with respect to FIG. 1B.

User interface 348 may be a graphical user interface (GUI), a text-based user interface, and/or any other suitable type of interface through which a user may provide input. For example, in some embodiments, the user interface may be a webpage or web application accessible through an Internet browser. In some embodiments, the user interface may be a graphical user interface (GUI) of an app executing on the user's mobile device. In some embodiments, the user interface may include a number of selectable elements through which a user may interact. For example, the user interface may include dropdown lists, checkboxes, text fields, or any other suitable element.

In some embodiments, machine learning classifier training module 352, referred to herein as training module 352, is configured to train the one or more machine learning classifiers used to identify candidate molecular categories for the biological sample. This may include training a machine learning classifier to determine the probability that the biological sample belongs to a particular molecular category. In some embodiments, the training module 352 trains a machine learning classifier using a training set of expression data. For example, the training module 352 may obtain training data via data store interface module 342. In some embodiments, the training module 352 may provide trained machine learning classifiers to the machine learning classifier data store 354 via data store interface module 342. Techniques for training machine learning classifiers are described herein including at least with respect to FIGS. 8A-B.

FIGS. 4A-4C show flowcharts of illustrative processes (e.g., processes 400, 420, and 440) for identifying at least one candidate molecular category for a biological sample using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein. The processes may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, computing device 104 as described herein with respect to FIG. 1A, computing device 304 as described herein with respect to FIG. 3 , computing device 1000 as described herein with respect to FIG. 10 , or in any other suitable way.

Shown in FIG. 4A, process 400 begins at act 402 for obtaining expression data previously obtained by processing a biological sample obtained from a subject. In some embodiments, the expression data includes any suitable expression data, such as expression data described herein including at least with respect to FIG. 1A and the section “Expression Data”. For example, the expression data may include RNA and/or DNA expression data.

In some embodiments, the expression data is obtained using any suitable techniques from any suitable location. For example, the expression data may be obtained from a data store (e.g., expression data store 346 of FIG. 3 ). For example, the expression data may have been previously obtained in a remote setting and uploaded to the data store. Additionally or alternatively, the expression data may be obtained directly from a sequencing platform (e.g., sequencing platform 344 of FIG. 3 ) used to previously obtain the expression data.

At act 404, the process 400 includes processing the expression data using the hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs. In some embodiments, the processing includes processing the expression data to obtain one or more features form the expression data. For example, the features may be derived from and/or inferred from the expression data obtained at act 404. In some embodiments, different features are obtained depending on the type of expression data obtained at act 404. For example, RNA features may be obtained from RNA expression data, while DNA features may be obtained from DNA expression data. Example RNA features and DNA features are described herein including at least with respect to FIGS. 6A-6B.

In some embodiments, the obtained features include a subset of features for a particular molecular category. The subset of features may include features that are unique to the molecular category. For example, as shown in FIG. 2A, feature B includes features unique to molecular category B.

In some embodiments, after obtaining features from the expression data, the processing includes applying at least one machine learning classifier of the hierarchy of machine learning classifiers to the obtained features. In some embodiments, this includes processing the features associated with a particular molecular category using at least one machine learning classifier in the hierarchy of machine learning classifiers to obtain an output indicative of whether to identify the molecular category as a candidate molecular category for the biological sample. For example, as shown in FIG. 2A, machine learning classifier B is used to process features B to determine whether to identify molecular category B as a candidate molecular category for the biological sample.

In some embodiments, as a result of processing the features, a machine learning classifier of the hierarchy of machine learning classifiers outputs a probability that the biological sample belongs to a particular molecular category. Additionally or alternatively, the machine learning classifier outputs a probability that the biological sample does not belong to the particular molecular category and/or a probability that the biological sample includes normal tissue from the site where the biological sample was obtained. For example, FIG. 2C illustrates a diagram of an example machine learning classifier used to predict between “Molecular Category B,” “Not Molecular Category B,” and (optionally) “Normal.”

At act 406, the process 400 includes identifying, using at least some of the machine learning classifier outputs, at least one candidate molecular category for the biological sample. In some embodiments, identifying the at least one candidate molecular category for the biological sample includes evaluating the probabilities indicated by the machine learning classifier outputs. For example, this may include comparing the probabilities to a threshold. In some embodiments, if a probability does not exceed the threshold, then the candidate molecular category associated with the machine learning classifier that output the probability is excluded from the candidate molecular categories identified for the biological sample. Conversely, if the probability does exceed the threshold, then the candidate molecular category associated with the machine learning classifier that output the probability may be included in the candidate molecular categories identified for the biological sample. Additionally or alternatively, in some embodiments, probabilities indicated by the output of machine learning classifiers at a same level of the hierarchy may be compared to one another. In some embodiments, molecular categories associated with machine learning classifiers that output the N (e.g., 1, 2, 3, etc.) greatest probabilities are included are identified as the candidate molecular categories for the biological sample.

FIG. 4B shows a flowchart of an illustrative process 420 for identifying at least one candidate molecular category for a biological sample using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein.

Process 420 begins at act 422, which includes obtaining RNA expression data including first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes. In some embodiments, the RNA expression data includes any suitable RNA expression data, such as the RNA expression data described herein including at least with respect to FIG. 1A and the section “Expression Data”.

In some embodiments, the RNA expression data includes expression level values for a number of genes. For example, the first RNA expression data includes first RNA expression level values for a first set of genes and the second RNA expression data includes second RNA expression level values for the second set of genes. In some embodiments, the first set of genes and second set of genes overlap, meaning that they share some of the same genes. In some embodiments, the first and second sets of genes do not overlap, meaning they do not share any of the same genes.

In some embodiments, the RNA expression data is obtained using any suitable techniques from any suitable location. For example, the RNA expression data may be obtained from a data store (e.g., expression data store 346 of FIG. 3 ). For example, the RNA expression data may have been previously obtained in a remote setting and uploaded to the data store. Additionally or alternatively, the RNA expression data may be obtained directly from a sequencing platform (e.g., sequencing platform 344 of FIG. 3 ) used to previously obtain the RNA expression data.

At act 424, the techniques include processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs. In some embodiments, the hierarchy molecular categories includes a parent molecular category and first and second molecular categories that are children of the parent molecular category. In some embodiments, the hierarchy of RNA-based machine learning classifiers includes a first RNA-based machine learning classifier used to obtain a first output that indicates whether the first molecular category is a candidate molecular category for the biological sample. In some embodiments, the hierarchy of RNA-based machine learning classifiers includes a second RNA-based machine learning classifier used to obtain a second output that indicates whether the second molecular category is a candidate molecular category for the biological sample.

In some embodiments, act 424 includes sub-act 424 a and sub-act 424 b. Sub-act 424 a includes processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output.

In some embodiments, processing the first RNA expression data includes processing the first RNA expression data to obtain a first set of RNA features. In some embodiments, as described herein, this includes ranking genes in the first set of genes based on the RNA expression level values associated with the first set of genes. In some embodiments, genes are ranked in ascending or descending order according to their expression level values. For example, the genes in the first set of genes may be assigned a value (e.g., 1, 2, 3, etc.) based on its expression level value. In some embodiments, the assigned values are different from the expression level values. Techniques for ranking genes are described herein including at least with respect to FIG. 6A.

In some embodiments, the first RNA-based machine learning classifier is applied to the obtained RNA features (e.g., the ranked gene sets). In some embodiments, this includes processing the obtained ranked gene set using the first RNA-based machine learning classifier to obtain the first output. In some embodiments, the first output is indicative of the probability that the biological sample belongs to the first molecular category corresponding to the first RNA-based machine learning classifier.

Sub-act 424 b includes processing second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether second molecular category is a candidate molecular category for the biological sample from which RNA expression data was obtained.

In some embodiments, processing the second RNA expression data includes processing the second RNA expression data to obtain a second set of RNA features. In some embodiments, as described herein, this includes ranking genes in the second set of genes based on the RNA expression level values associated with the second set of genes. In some embodiments, genes are ranked in ascending or descending order according to their expression level values. For example, the genes in the second set of genes may be assigned a value (e.g., 1, 2, 3, etc.) based on its expression level value. In some embodiments, the assigned values are different from the expression level values.

In some embodiments, the second RNA-based machine learning classifier is applied to the obtained RNA features (e.g., the ranked gene sets). In some embodiments, this includes processing the obtained ranked gene set using the second RNA-based machine learning classifier to obtain a second output. In some embodiments, the second output is indicative of the probability that the biological sample belongs to the second molecular category corresponding to the second RNA-based machine learning classifier.

At act 426, process 420 includes identifying, using at least some of the RNA-based machine learning classifier outputs, including the first output and the second output, at least one candidate molecular category for the biological sample. In some embodiments, as described above, including at least with respect to FIG. 4A, identifying the at least one candidate molecular category for the biological sample includes evaluating the probabilities indicated by the RNA-based machine learning classifier outputs. For example, this may include comparing the probabilities to a threshold. In some embodiments, if a probability does not exceed the threshold, then the candidate molecular category associated with the RNA-based machine learning classifier that output the probability is excluded from the candidate molecular categories identified for the biological sample. Conversely, if the probability does exceed the threshold, then the candidate molecular category associated with the RNA-based machine learning classifier that output the probability may be included in the candidate molecular categories identified for the biological sample. Additionally or alternatively, in some embodiments, probabilities indicated by the output of RNA-based machine learning classifiers at a same level of the hierarchy may be compared to one another. For example, this may include comparing the first and second outputs. In some embodiments, molecular categories associated with RNA-based machine learning classifiers that output the N (e.g., 1, 2, 3, etc.) greatest probabilities are included are identified as the candidate molecular categories for the biological sample. For example, one of the first and second molecular categories may be identified for the biological sample based on how they compare to one another.

FIG. 4C shows a flowchart of an illustrative process 440 for identifying at least one candidate molecular category for a biological sample using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories, according to some embodiments of the technology described herein.

Process 440 begins at act 442, which includes obtaining DNA expression data including first DNA expression data and second DNA expression data. In some embodiments, the DNA expression data includes any suitable DNA expression data, such as the DNA expression data described herein including at least with respect to FIG. 1A and the section “Expression Data”.

In some embodiments, the DNA expression data is obtained using any suitable techniques from any suitable location. For example, the DNA expression data may be obtained from a data store (e.g., expression data store 346 of FIG. 3 ). For example, the DNA expression data may have been previously obtained in a remote setting and uploaded to the data store. Additionally or alternatively, the DNA expression data may be obtained directly from a sequencing platform (e.g., sequencing platform 344 of FIG. 3 ) used to previously obtain the DNA expression data.

At act 444, the techniques include processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs. In some embodiments, the hierarchy molecular categories includes a parent molecular category and first and second molecular categories that are children of the parent molecular category. In some embodiments, the hierarchy of DNA-based machine learning classifiers includes a first DNA-based machine learning classifier used to obtain a first output that indicates whether the first molecular category is a candidate molecular category for the biological sample. In some embodiments, the hierarchy of DNA-based machine learning classifiers includes a second DNA-based machine learning classifier used to obtain a second output that indicates whether the second molecular category is a candidate molecular category for the biological sample.

In some embodiments, act 444 includes sub-act 444 a and sub-act 424 b. Sub-act 444 a includes processing the first DNA expression data using a first DNA-based machine learning classifier to obtain the first output indicative of whether first molecular category is a candidate molecular category for the biological sample from which DNA expression data was obtained.

In some embodiments, processing the first DNA expression data includes processing the first DNA expression data to obtain a first set of DNA features. In some embodiments, as described herein, this includes generating numeric and/or binary data that quantifies and/or identifies information contained in the first DNA expression data. Example DNA features are described herein including at least with respect to FIG. 6B.

In some embodiments, the first DNA-based machine learning classifier is applied to the obtained DNA features. In some embodiments, this includes processing the obtained features using the first DNA-based machine learning classifier to obtain the first output. In some embodiments, the first output is indicative of the probability that the biological sample belongs to the first molecular category corresponding to the first DNA-based machine learning classifier.

Sub-act 444 b includes processing second DNA expression data using second DNA-based machine learning classifier to obtain the second output indicative of whether second molecular category is a candidate molecular category for the biological sample from which DNA expression data was obtained.

In some embodiments, processing the second DNA expression data includes processing the second DNA expression data to obtain a second set of DNA features. In some embodiments, as described herein, this includes generating numeric and/or binary data that quantifies and/or identifies information contained in the second DNA expression data. Example DNA features are described herein including at least with respect to FIG. 6B.

In some embodiments, the second DNA-based machine learning classifier is applied to the obtained DNA features. In some embodiments, this includes processing the obtained features using the second DNA-based machine learning classifier to obtain the second output. In some embodiments, the second output is indicative of the probability that the biological sample belongs to the second molecular category corresponding to the second DNA-based machine learning classifier.

At act 446, process 440 includes identifying, using at least some of the DNA-based machine learning classifier outputs, including the first output and the second output, at least one candidate molecular category for the biological sample. In some embodiments, as described above, including at least with respect to FIG. 4A, identifying the at least one candidate molecular category for the biological sample includes evaluating the probabilities indicated by the DNA-based machine learning classifier outputs. For example, this may include comparing the probabilities to a threshold. In some embodiments, if a probability does not exceed the threshold, then the candidate molecular category associated with the DNA-based machine learning classifier that output the probability is excluded from the candidate molecular categories identified for the biological sample. Conversely, if the probability does exceed the threshold, then the candidate molecular category associated with the DNA-based machine learning classifier that output the probability may be included in the candidate molecular categories identified for the biological sample. Additionally or alternatively, in some embodiments, probabilities indicated by the output of DNA-based machine learning classifiers at a same level of the hierarchy may be compared to one another. For example, this may include comparing the first and second outputs. In some embodiments, molecular categories associated with DNA-based machine learning classifiers that output the N (e.g., 1, 2, 3, etc.) greatest probabilities are included are identified as the candidate molecular categories for the biological sample. For example, one of the first and second molecular categories may be identified for the biological sample based on how they compare to one another.

FIG. 5A-1 is an example 500 for processing RNA expression data obtained from a biological sample to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

In some embodiments, the techniques include processing the RNA expression data 501 to obtain RNA features A 502, RNA features B 513 a, RNA features C 514 a, RNA features D 515 a, RNA features E 516 a, and RNA features F 517 a. Example RNA features are described herein including at least with respect to FIG. 6A.

In some embodiments, the RNA-based machine learning classifiers of the hierarchy of RNA-based machine learning classifiers are used to process the features to determine whether to identify the molecular category associated with the machine learning classifier as a candidate molecular category for the biological sample. For example, RNA classifier B 513 b is used to process RNA features B 513 a to determine whether to identify molecular category B as a candidate molecular category for the biological sample. Similarly, classifier C 514 b, classifier D 515 b, classifier E 516 b, and classifier F 517 b are each used to process respective features B-F.

In some embodiments, the output of the RNA-based machine learning classifiers indicates the probability 535, 536, and 537 that the biological sample belong to each particular molecular category. As described above, including at least with respect to FIG. 2D, the probabilities at each level may be compared to a threshold and/or compared to one another to determine whether to identify a molecular category as a candidate molecular category for the biological sample.

As shown in FIG. 5A-1 , candidate molecular category A 535 a, candidate molecular category C 536 a, and candidate molecular category F 537 a are identified for the biological sample in this example.

FIG. 5A-2 is an example 550 for processing DNA expression data obtained from a biological sample to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

In some embodiments, the techniques include processing the DNA expression data 541 to obtain DNA features A 542, DNA features B 553 a, DNA features C 554 a, DNA features D 555 a, DNA features E 556 a, and DNA features F 557 a. Example DNA features are described herein including at least with respect to FIG. 6A.

In some embodiments, the DNA-based machine learning classifiers of the hierarchy of DNA-based machine learning classifiers are used to process the features to determine whether to identify the molecular category associated with the machine learning classifier as a candidate molecular category for the biological sample. For example, DNA classifier B 553 b is used to process DNA features B 553 a to determine whether to identify molecular category B as a candidate molecular category for the biological sample. Similarly, classifier C 554 b, classifier D 555 b, classifier E 556 b, and classifier F 557 b are each used to process respective features B-F.

In some embodiments, the output of the DNA-based machine learning classifiers indicates the probability 565, 566, and 567 that the biological sample belong to each particular molecular category. As described above, including at least with respect to FIG. 2D, the probabilities at each level may be compared to a threshold and/or compared to one another to determine whether to identify a molecular category as a candidate molecular category for the biological sample.

As shown in FIG. 5A-2 , candidate molecular category A 565 a and candidate molecular category C 566 a are identified for the biological sample in this example.

Combining RNA and DNA Hierarchical Outputs

FIG. 5B illustrates an example 570 for using the output of the hierarchy of RNA-based machine learning classifiers and the output of the hierarchy of DNA-based machine learning classifiers to identify at least one candidate molecular category for the biological sample, according to some embodiments of the technology described herein.

As shown in the example of FIGS. 5A-1-5A-2 , a hierarchy of RNA-based machine learning classifiers 500 and a hierarchy of DNA-based machine learning classifiers 550 are each used to identify candidate molecular categories for a biological sample. In some embodiments, such as in this example, there may be differences in the molecular categories (e.g., categories “A,” “C” and “F” output by the RNA-based hierarchy and categories “A” and “C” output by the DNA-based hierarchy) identified by the two hierarchies.

In some embodiments, such difference between outputs may arise due to differences between the RNA expression data and the DNA expression data processed using the hierarchies. For example, the sample purity may affect the data and therefore affect (e.g., invalidate) the predictions output by one or both of the classifiers. In particular, the sample purity may influence the output of classifiers trained to process RNA expression data. For example, if the sample purity is high, an RNA-based machine learning classifier may yield a more accurate and/or reliable result because the signal is improved. By contrast, if sample purity is low, the RNA-based classifier may yield a less accurate and/or reliable result (and therefore a DNA classifier may be more reliable). Additionally or alternatively, site from which the biological sample was obtained may affect at least the RNA-based machine learning classifier outputs 572. In particular, as explained above including at least with respect to FIG. 2D, the outputs 572 may be biased towards molecular categories that are associated with clinical diagnoses originating from the sample site.

Accordingly, the inventors have developed techniques that account for these discrepancies. As shown in FIG. 5B, in some embodiments, the techniques include identifying final probabilities 577 for the molecular categories based on the RNA-based machine learning classifier outputs 572 and the DNA-based machine learning classifier outputs 573.

In some embodiments, identifying the final probabilities 577 includes processing the RNA-based machine learning classifier outputs 572 and the DNA-based machine learning classifier outputs 573 using model 576. In some embodiments, model 576 is used to combine outputs 572 and outputs 573, such that the final probabilities 577 differ. For example, as shown in FIG. 5B, the final probabilities 577 differ from outputs 572 and 573. In some embodiments, model 576 may implement machine learning techniques to combine outputs 572 and 573. For example, model 576 may include a neural network, a Naïve Bayes model, a linear regression model, or any suitable machine learning model, as aspects of the technology are not limited in this respect. In some embodiments, model 576 may include calculating an average or a weighted average of the outputs 572 and 573.

Additionally or alternatively, in some embodiments, model 576 may select between the RNA-based classifier outputs 572 and the DNA-based classifier outputs 576. For example, this may include selecting either output 572 or output 573 for final probabilities 577. Additionally or alternatively, this may include selectively identifying probabilities from among outputs 572 and 572 to be used as final probabilities 577.

In some embodiments, discrepancies between outputs 572 and 573 arise due to differences between the RNA and DNA expression data processed using the hierarchy of RNA-based and the hierarchy of DNA-based machine learning classifiers, respectively. For example, the sample purity may affect the data and therefore affect (e.g., invalidate) the predictions output by one or both of the classifiers. In particular, the sample purity may influence the output of classifiers trained to process RNA expression data. For example, if the sample purity is high, an RNA-based machine learning classifier may yield a more accurate and/or reliable result because the signal is improved. By contrast, if sample purity is low, the RNA-based classifier may yield a less accurate and/or reliable result (and therefore a DNA classifier may be more reliable). Accordingly, in some embodiments, model 576 may consider sample purity 574 in determining final probabilities 577. For example, the model 576 may apply different weights to probabilities 572 and 573 when the sample purity is high versus when the sample purity is low. In particular, when the sample purity is high, a greater weight may be applied to the RNA-based machine learning classifier outputs 572 (and vice versa). Additionally or alternatively, sample purity 574 can be used to exclude probabilities included in outputs 572 and/or 573 from final probabilities 577.

Additionally or alternatively, site from which the biological sample was obtained may affect at least the RNA-based machine learning classifier outputs 572. In particular, as explained above including at least with respect to FIG. 2D, the outputs 572 may be biased towards particular molecular categories that are associated with clinical diagnoses originating from the sample site. Accordingly, in some embodiments, the model 576 considers the sample site 574 in determining final probabilities 577. In some embodiments, the probabilities corresponding to molecular categories that are associated with clinical diagnoses corresponding to the sample site may be considered with less weight. For example, the probabilities corresponding to the molecular category “Lung Neoplasm” (e.g., as shown in FIG. 7A-1 ) may be considered with less weight when the biological sample is obtained from the lung.

In some embodiments, the final probabilities 577 are used to identify candidate molecular categories 578 (e.g., according to the techniques described herein including at least with respect to FIG. 2D. Additionally or alternatively, the candidate molecular categories 578 are obtained directly from model 576 (e.g., without determining final probabilities 577).

Output Correction Techniques

In some embodiments, as described above, the output of a machine learning classifiers in the hierarchy of machine learning classifiers is indicative of a probability that the biological sample belong to a particular molecular category. In some embodiments, the machine learning classifier accounts for the probability that the biological sample belongs to another molecular category at the same level of the molecular category (e.g., “not molecular category A” as shown in FIG. 2D).

However, since a machine learning classifier is trained, in some embodiments, to independently predict whether to identify a corresponding molecular category as a candidate molecular category for the biological sample, it does not account for probabilities output by other machine learning classifiers in the hierarchy, resulting in mispredictions. For example, as shown in FIG. 5C, classifier 582 outputs a probability of 0.04, while classifier 584 b outputs a probability of 0.7. Since classifier 584 b is corresponds to a molecular category that descends from the molecular category corresponding to classifier 582, it should output a lower probability than the probability output by classifier 582.

Accordingly, one or more correction techniques may be applied to the probabilities output by the classifiers, after at least some of the classifiers have made their predictions. In some embodiments, the techniques include multiplying a probability output by a classifier at a lower level of the hierarchy by a probability output by a classifier at an upper level of the classifier. However, it should be appreciated that any suitable correction technique may be used to correct for mispredictions, as aspects of the technology described herein are not limited to any particular correction technique.

FIGS. 5C-5D shows an example of correcting for probabilities output by machine learning classifiers of the hierarchy of machine learning classifiers.

In the example shown in FIG. 5C, the probabilities output by classifiers 584 a-c, of hierarchy 580 a, are each multiplied by the probability output by classifier 582. The results are shown with respect to hierarchy 580 b. In particular, P=0.4 is multiplied by P=0.97 to obtain P=0.388, P=0.8 is multiplied by P=0.97 to obtain P=0.776, and P=0.04 is multiplied by P=0.97 to obtain P=0.034.

In the example shown in FIG. 5D, the probabilities output by classifiers 584 a-c, of hierarchy 580 c, are each multiplied by the probability output by classifier 582. The results are shown with respect to hierarchy 580 d. In particular, P=0.3 is multiplied by P=0.04 to obtain P=0.012, P=0.7 is multiplied by P=0.04 to obtain P=0.028, and P=0.1 is multiplied by P=0.04 to obtain P=0.004.

As described above, in some embodiments, molecular categories associated with classifiers that output probabilities that exceed a threshold may be identified as a candidate molecular category for the biological sample, while others will be excluded. Here, with respect to hierarchy 580 c, classifier 584 b output a probability that exceeded an example threshold of 0.5 before the application of the correction techniques. However, after the application of such techniques, the probability does not exceed the example threshold, and will thus be excluded from further analysis.

Example RNA and DNA Features

FIG. 6A is a diagram showing example RNA expression data 610 and example RNA features obtained from the RNA expression data, according to some embodiments of the technology described herein.

In some embodiments, RNA expression data 610 includes gene expression levels for multiple genes. For example, RNA expression data 610 includes gene expression levels 612 a for a first set of genes (e.g., genes A-D) and gene expression levels 612 b for a second set of genes (e.g., genes E-H). However, it should be appreciated that gene sets described herein are not limited to any particular number of genes, as aspects of the technology described herein are not limited in this respect. In some embodiments, different sets of genes may share one or more of the same genes or may not share any of the same genes. Techniques for determining which genes are to be included in a set of genes are described herein including at least with respect to FIG. 8A.

In some embodiments, RNA expression data 610 may be processed to obtain one or more RNA features 620. In some embodiments, processing the RNA expression data includes ranking genes in a gene set (e.g., gene sets A-D) based on the expression levels of the genes. In some embodiments, the genes may be ranked in ascending order, such that genes associated with relatively low expression values are assigned lower ranks, while genes associated with relatively high expression values are assigned higher ranks. However, it should be appreciated that genes could be ranked in descending order, as aspects of the technology are not limited in this respect. Example techniques for ranking genes are described in U.S. patent application Ser. No. 17/113,008, titled “MACHINE LEARNING TECHNIQUES FOR GENE EXPRESSION ANALYSIS”, filed on Dec. 5, 2020, which is incorporated by reference herein in its entirety.

FIG. 6A shows an example of ranking genes in based on expression levels 612 a. As shown, gene C corresponds to the lowest expression level value (e.g., 0.02) out of RNA expression data associated with gene set 612 a. Therefore, gene C is assigned to a rank of 1, as shown in the rank transformed data 622 a. By contrast, gene B corresponds to the highest expression level value (e.g., 0.32) out of RNA expression data for gene set 612 a. Therefore, gene B is assigned a rank of 4, as shown in rank transformed data 622 b. However, it should be appreciated that genes could be ranked in descending order, as aspects of the technology are not limited in this respect.

In some embodiments, the same expression level value may be measured for different genes. For example, genes E and G share the same expression level value (e.g., 0.20). Such genes may be assigned an average rank of all ranks corresponding to those genes. As shown in the example, genes E and G would correspond to ranks 2 and 3, and the average rank of 2.5 would be applied to both genes.

FIG. 6B is a diagram showing example DNA expression data 651 and example DNA features 652 obtained from the DNA expression data 651, according to some embodiments of the technology described herein.

In some embodiments, DNA expression data 651 is processed to obtain DNA features 652. For example, the DNA features 652 may be derived and/or inferred from the DNA expression data 651 according to any suitable technique, as aspects of the technology are not limited in this respect. For example, one or more bioinformatics software packages may be used to calculate one or more of the DNA features from DNA expression data.

Non-limiting examples of DNA features 652 include one or more features 654 indicative of the presence of one or more mutations, one or more features 655 indicative of copy number alterations (CNA), one or more feature 655 c indicative of ploidy, one or more features 656 indicative of the presence of one or more gene fusions 656, one or more features 657 indicative of microsatellite instability (MSI) status, one or more features indicative of presence of protein-coding genes, and/or any other suitable features that may be derived and/or inferred from DNA sequence data, as aspects of the technology described herein are not limited in this respect.

In some embodiments, the one or more features 654 indicative of the presence of one or more mutations f encompasses one or more DNA features that relate to genetic mutations, including, but not limited to, one or more features indicative of the presence of one or more pathogenic gene mutations 654 a, one or more features indicative of the presence of one or more mutational hotspots 654 b, and a feature indicative of the tumor mutational burden (TMB) 654 c. A feature indicative of the presence of a pathogenic gene mutation or a mutational hotspot may be a binary feature taking on one of two values, with one of the values (e.g., the numerical value “1” or the categorical value “True”) indicating the presence of that type of mutation and the other one of the values (e.g., the numerical value “0” or the categorical value “False”) indicating the absence of that type of mutation.

In some embodiments, the gene mutations feature(s) 654 a may be indicative of the presence of one or more alterations in the DNA expression data relative to a reference genome. For example, a gene mutation may be a nonsense mutation, a frame shift insertion, a frame shift deletion, an in-frame insertion, an in-frame deletion, a non-stop mutation, or a missense mutation. In some embodiments, to obtain data indicative of the gene mutations 654 a, the mutations 654 a may be encoded in the form of a binary vector, where 1 indicates the presence of a mutation in a gene, and 0 indicates the absence of a mutation in a gene.

In some embodiments, the gene mutations 654 may be pre-filtered. In some embodiments, gene mutations 654 a may be pre-filtered by classification-type variant allele frequency (VAF), such that only those mutations with a VAF that exceeds a threshold may be considered for further analysis. For example, the VAF threshold may be at least 0.2, at least 0.3, at least 0.4, at least 0.5, at least 0.6, at least 0.7, or any other suitable threshold VAF, as aspects of the technology are not limited in this respect. Additionally or alternatively, the gene mutations 654 a may be pre-filtered by pathogenicity such that only pathogenic mutations remain. For example, the genetic mutations may be pre-filtered by pathogenicity using the techniques described in Sarachakov et. al. (MutAnt: Mutation annotation machine learning algorithm for pathogenicity evaluation of single nonsynonymous nucleotide substitutions in cancer cells, in Proc. of the AACR Annual Meeting 2021, Cancer Res., 81(13 Suppl.), 192), which is incorporated herein by reference in its entirety. It should be appreciated that any other suitable techniques may be used to filter the gene mutations 654 a, as aspects of the technology are not limited in this respect.

Mutational hotspots 654 b are nucleotide positions with an exceptionally high mutation frequency. In some embodiments, two different features may reflect mutational hotspots. The first feature may indicate the presence of a mutation in a certain position in a certain protein (e.g., where the position is a known hotspot site). For example, the feature may be a binary feature, where 1 represents the presence of the mutation at the position and 0 represents the absence of the mutation at the position. The second feature may indicate the presence of any known hotspot(s) in the gene. For example, this may also be a binary feature, where 1 represents the presence of the hotspot(s) and 0 represents the absence of the hotspot(s). In some embodiments, hotspot features are generated from mutations in any suitable file format, such as mutation annotation format (MAF) or variant call format (VCF), as aspects of the technology are not limited in this respect.

Tumor mutational burden (TMB) 654 c is a feature that is indicative of an amount of gene mutation that occurs in the genome. In some embodiments, determining TMB 654 c includes determining the number of nonsynonymous somatic mutations per coding region of a tumor genome. For example, the techniques may include determining the total number of nonsynonymous somatic mutations per 1 MB of used whole-exome sequencing (WES) data. In some embodiments, all nonsynonymous somatic coding mutations having a coverage greater than a threshold may be included in the total number. For example, nonsynonymous somatic coding mutations having a coverage greater than 15×, 25×, 35×, or 45× may be included in the total number. Additionally or alternatively, all nonsynonymous somatic coding mutations having an allelic fraction greater than a threshold may be included in the total number. For example, nonsynonymous somatic coding mutations having an allelic fraction greater than 2%, 4%, 5%, 6%, 8%, or 10% may be included in the total number.

In some embodiments, copy number alterations (CNA) feature category 655 encompasses features related to CNA, including, but not limited to, CNA genes 655 a, CNA and loss of heterozygosity (LOH) values 655 b, and ploidy 655 c. In some embodiments, CNA genes 655 a include deletions or amplifications of portions of the genome. In some embodiments, features, such as the normalized gene copy number, are derived from the CNA genes. For example, Bagaev et. al. (Integrated whole exome and transcriptome analyses of the tumor and microenvironment provide new opportunities for rational design of cancer therapy, in Proc. of the AACR Annual Meeting 2020, Cancer Res., 80(16 Suppl.), 4418), which is incorporated herein by reference in its entirety, describes determining normalized gene copy numbers.

In some embodiments, the techniques include determining CNA and/or LOH values 655. In some embodiments, this may first include splitting a chromosome into bins. In some embodiments, this may include splitting the chromosome into bins of equal length, where the length is any suitable length, as aspects of the technology are not limited in this respect. For example, the bin length may be 106 base pairs (bp), 107 bp, or 108 bp. Additionally or alternatively, the chromosome may be split into arms (e.g., the “p arm” and “q arm”). Additionally or alternatively, the chromosome may not be split.

In some embodiments, the techniques include determining values for each of the bins based on average copy number and/or loss of heterozygosity (LOH). For example, determining the average copy number value for a bin (or arm or chromosome) may include determining the weighted average of the normalized copy number of all segments that intersect with the bin (or arm or chromosome), where the weight of the segment is the length of the intersection, as shown in Equation 1.

$\begin{matrix} {{{CNA}{Value}} =} & \left( {{Equation}1} \right) \end{matrix}$ $\frac{\sum\left( {{Normalized}{Copy}{Number} \times {Intersection}{Length}} \right)}{{Bin},{Arm},{{Chromosome}{Length} \times {Number}{of}{Intersections}}}$

Similarly, determining the LOH value for a bin (or arm or chromosome) may include determining the weighted average of the LOH values of all segments that intersect with the bin (or arm or chromosome), where the weight of the segment is the length of the intersection, as shown in Equation 2.

$\begin{matrix} {{{LOH}{Value}} =} & \left( {{Equation}2} \right) \end{matrix}$ $\frac{\sum\left( {{LOH}{Value} \times {Intersection}{Length}} \right)}{{Bin},{Arm},{{Chromosome}{Length} \times {Number}{of}{Intersections}}}$

Ploidy 655 c refers to the number of complete sets of chromosomes in a cell. For example, except for gametes, healthy human cells have two sets of homologous chromosomes (e.g., diploid). By contrast, some cancer cells may contain more than two sets of homologous chromosomes (e.g., polyploid). In some embodiments, any suitable technique may be used to calculate ploidy, as aspects of the technology described herein are not limited in this respect. Example algorithms for determining ploidy are described by Favero et. al. (Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data, Ann. Oncol., 26(1): 64-70) and Shen, R. & Seshan, V. E. (FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high throughput DNA sequencing, Nucleic Acids Res., 44(16): e131), each of which is incorporated by reference herein in its entirety.

Gene fusions 656 are hybrid genes that form as a result of chromosomal rearrangements (e.g., translocations, deletions, etc.). In some embodiments, there may be several types of fusion features. A first example includes the fusion of a first gene (e.g., gene A) with a second gene (e.g., gene B). A second example includes the fusion of the first type of gene (e.g., gene A) with any gene. A third example includes the fusion of any gene with the first type of gene (e.g., gene A). It should be appreciated that, due to the nature of fusion, the order is important, and thus the second example differs from the third example. In some embodiments each type of feature may be represented in binary format, where 1 represents the presence of a fusion and 0 represents the absence of the fusion.

Microsatellite instability (MSI) status 657 is a condition in which the number of repeated DNA based in a short, repeated sequence of DNA (a microsatellite) differs from what it was when the microsatellite was inherited. In some embodiments, MSI status 657 may be represented by a binary feature, where 1 represents instability and 0 represents stability. In some embodiments, MSI status may be procured by laboratory analysis, sequencing analysis, or any other suitable technique, as aspects of the technology described herein are not limited to any particular procurement technique.

In some embodiments, genes 658 include protein-coding and non-protein coding genes. In some embodiments, features, such as the normalized gene copy number, are derived from the genes. For example, Bagaev et. al. (Integrated whole exome and transcriptome analyses of the tumor and microenvironment provide new opportunities for rational design of cancer therapy, in Proc. of the AACR Annual Meeting 2020, Cancer Res., 80(16 Suppl.), 4418), which is incorporated herein by reference in its entirety, describes determining normalized gene copy numbers.

While examples of features that can be derived from DNA expression data have been described above, it should be appreciated that this is a non-exhaustive list and any suitable feature may be used in addition to or instead of the features described above.

Example Hierarchies of Molecular Categories

FIGS. 7A-1-7A-3 and FIGS. 7B-1-7B-5 show example hierarchies of molecular categories that could be used in conjunction with the techniques described herein. Table 2 lists the molecular categories shown in FIGS. 7A-1-7A-3 and FIGS. 7B-1-7B-5 However, it should be appreciated that other suitable hierarchies of molecular categories may be used, as the techniques described herein are not limited to any particular labelling of molecular categories or relationships between molecular categories.

In these examples, a molecular category is a category of biological samples that share features from Hoadley et. al. (Cell-Of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer, Cell, 173(2), 291-304), Robinson et. al. (Integrative clinical genomics of metastatic cancer, Nature, 548, 297-303), and Hoadley et. al. (Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin, Cell, 158(4), 929-944), each of which is incorporated herein by reference in its entirety.

FIGS. 7A-1-7A-3 show an example hierarchy 700 of molecular categories, according to some embodiments of the technology described herein. As shown, molecular categories are represented by nodes, and relationships between the molecular categories are represented by edges that connect the nodes. For example, the molecular category “Neoplasm” shown in FIG. 7A-2 is represented by a parent node that has child nodes representing molecular categories “Hematologic Neoplasm” and “Solid Neoplasm.” As another example, the node representing “Renal Cell Carcinoma” shown in FIG. 7A-2 is a parent node to child nodes that represent the molecular categories “Non-Clear Cell Carcinoma” and “Clear Cell Carcinoma,” also shown in FIG. 7A-2 .

As described above, molecular categories at different levels of the hierarchy have differing degrees of specificity—molecular categories at higher levels of the hierarchy are broader categories and have lower specificity, while molecular categories at lower levels of the hierarchy are narrower categories having higher specificity. For example, the molecular category “Adenocarcinoma” has a lower specificity than molecular category “Prostate Adenocarcinoma,” since it is at a higher level of the molecular category.

FIG. 7B-1-7B-5 show an example hierarchy 750 of molecular categories, according to some embodiments of the technology described herein. The example hierarchy 750 includes some molecular categories that are also included in 700 and some molecular categories that are not included in example hierarchy 700. For example, at least molecular categories “Hepatocellular Carcinoma” and “Cholangiocarcinoma” as shown in FIG. 7B-4 .

As explained above, it should be appreciated that any suitable hierarchy of molecular categories, including either example hierarchy 700 and/or example hierarchy 750, can be used in conjunction with the techniques described herein to identify a candidate molecular category, as aspects the technique are not limited in this respect.

TABLE 2 Example Molecular Categories Molecular category Neoplasm Solid Neoplasm Hematologic Neoplasm Melanoma Non-Uveal Melanoma Uveal Melanoma Non-Cutaneous Melanoma Cutaneous Melanoma Sarcoma Soft Tissue Sarcoma Osteosarcoma Mesothelioma Peritoneal Mesothelioma Pleural Mesothelioma Neuroendocrine Neuroendocrine Small Cell Small Cell Prostate Cancer Large Cell Neuroendocrine Carcinoma Small Cell Lung Carcinoma Squamous Cell Carcinoma Colorectal Squamous Cell Carcinoma Cutaneous Squamous Cell Carcinoma Adenocarcinoma Adrenocortical Carcinoma Glioma Adenoid Cystic Carcinoma Adenoid Cystic Carcinoma of the Uterine Cervix Adenoid Cystic Carcinoma of the Breast Salivary Gland Adenoid Cystic Carcinoma Testicular Germ Cell Tumor Pheochromocytoma Cervical Squamous Cell Carcinoma Liver Neoplasm Hepatocellular Carcinoma Cholangiocarcinoma Lung Adenocarcinoma High Grade Glioma IDH Mut Thyroid Neoplasm Merkel Cell Carcinoma Paraganglioma Gastrointestinal Neuroendocrine Neoplasm Squamous Cell Lung Carcinoma Thymoma Prostate Adenocarcinoma Urinary Bladder Urothelial Carcinoma Oligodendroglioma Squamous Cell Carcinoma of the Head and Neck Gastrointestinal Adenocarcinoma Gynecological Renal Cell Carcinoma Astrocytoma Pancreatic Adenocarcinoma Stomach Adenocarcinoma Pancreatic Adenocarcinoma Colorectal Adenocarcinoma of the Breast Breast Cancer Ovarian Cancer Uterine Corpus Endometrial Carcinoma Non-Clear Cell Carcinoma Clear Cell Carcinoma Basal Breast Cancer Non-Basal Breast Cancer

Training an RNA-Based Machine Learning Classifier

As described above, the machine learning techniques developed by the inventors include processing RNA expression data for a particular set of genes using a particular machine learning classifier to determine whether to identify a particular molecular category as a candidate molecular category for the biological sample. Illustrative process 800 shows a flowchart for identifying the particular set of genes and for training a machine learning classifier, according to some embodiments of the technology described herein. Process 800 may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, computing device 104 as described herein with respect to FIG. 1A, computing device 1000 as described herein with respect to FIG. 10 , or in any other suitable way.

Process 800 begins at act 802, where expression level values are obtained for a plurality of genes. In some embodiments, expression level values may be obtained using any suitable technique or combination of techniques, such as the techniques described herein including at least with respect to FIGS. 1A-B and in the “Expression Data” and “Obtaining RNA expression data” sections.

At act 804, the techniques include identifying an initial set of genes of the plurality of genes for which expression data was obtained at act 802. In some embodiments, identifying an initial set of genes includes identifying genes that distinguish the candidate molecular category from all other molecular categories. Additionally or alternatively, this may include identifying genes that distinguish the candidate molecular category from the normal tissue corresponding to the molecular category (e.g., normal tissue from the site of origin). In some embodiments, identifying such genes includes performing a differential expression analysis. In some embodiments, this included performing running a pairwise differential expression analysis between the candidate molecular category and all other molecular categories. Additionally or alternatively, this may include performing a pairwise differential expression analysis between the candidate molecular category and the normal tissue.

After performing the differential expression analysis, in some embodiments, genes that appear greater than a threshold number of times in the differential expression analysis are selected for the initial set of genes. For example, genes appearing greater than a threshold number of times may be selected for initial set of genes. In some embodiments, the initial set of genes includes at least 400 genes, at least 600 genes, at least 700 genes, at least 800 genes, at least 1,000 genes, at least 1,200, at least 1,400 genes, at least 1,500 genes, between 400 genes and 1,500 genes, or between 700 and 1,200 genes. In some embodiments, narrowing down the number of genes to an initial set of genes reduces the computational load required for further processing.

At act 806, the techniques include ranking the expression level values of the genes included in the initial set of genes. In some embodiments, ranking the genes according to their expression level values includes assigning a rank to each gene in the set based on the expression level value associated with that gene. In some embodiments, a rank is an integer that is different from the expression level value to which it has been assigned. Example techniques for ranking genes are described herein including at least with respect to FIG. 6A.

At act 808, the techniques include choosing hyperparameters and fitting a statistical model. In some embodiments, this includes performing cross-validation using any suitable techniques, such as, stratified k-fold cross validation. For example, a 5-fold stratified cross-validation may be used. In some embodiments, any suitable train to test ratio may be used, such as, for example, 80 to 20 percent. Pedregosa et. al. (Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12(85): 2825-2830) describes an algorithm for realizing a stratified k-fold cross validation.

In some embodiments, the hyperparameters are selected according to a weighted F1 score of a cross-validation. In some embodiments, the hyperparameters are selected according to a weighted F1 score of a cross-validation. Example hyperparameters include, but are not limited to number of estimators, number of leaver, learning rate, and share of features per one tree.

Equation 3 is an example formula for calculating an average weighted F1 score:

$\begin{matrix} {{{{Avg}.{Weighted}}F1} = {{\sum}_{classes}\frac{{class}{size}}{{total}{samples}}*\frac{2*{precision}_{class}*{recall}_{class}}{{precision}_{class} + {recall}_{class}}}} & \left( {{Equation}3} \right) \end{matrix}$

where class represents the target molecular category and class size represents the number of samples of the molecular category in the test dataset. In some embodiments, precision and recall for the molecular category are estimated on a full test data set, separated on two classes—the target molecular category and all other molecular categories (and, in some embodiments, normal tissue).

In some embodiments, two different weighted F1 scores are calculated. First, a weighted F1 score may be calculated considering cases where the machine learning classifier is unable to predict any molecular category (e.g., failed). Second, a weighted F1 score may be calculated that excludes failed predictions.

At act 810, process 800 includes calculating the importance each of genes in the initial set. This includes assigning a score to the gene based on how valuable it is in predicting the target variable. Gene importance can be calculated using any suitable method, as aspects of the technology described herein are not limited to any particular gene importance calculation technique. In some embodiments, regression coefficients may be used to determine gene importance (e.g., when using a linear regression classifier). In some embodiments, Gini importance may be used to determine gene importance (e.g., when using a gradient boosting classifier). In some embodiments. SHAP values may be used to determine gene importance (e.g., when using a gradient boosting tree classifier). For example, Lundberg et. al. (“From local explanations to global understanding with explainable AI for trees,” Nat Mach Intell 2, 56-57), which is incorporate herein by reference in its entirety, describes techniques for determining gene importance using SHAP values for gradient boosting tree classifiers,

At act 812, process 800 includes generating an updated set of the genes by discarding at least a threshold number of the least important genes, based on the calculated gene importances. For example, this may include discarding at least 1 gene, at least 2 genes, at least 5 genes, at least 8 genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, between 1 and 30 genes, between 2 and 15 genes, between 2 and 5 genes, or between 5 and 10 genes. In some embodiments, the number of genes discarded depends on the number of genes included in the gene set. For example, more genes with be discarded when the gene set is relatively large compared to the number of genes discarded with the initial gene set is relatively small.

At act 814, process 800 includes determining whether there are more genes remaining in the gene set, which was updated at act 812. If there are genes remaining in the gene set, process 800 returns to act 808, where ranks are assigned to genes in the updated gene set. If there are no genes remaining in the set, process 800 proceeds to act 816.

At act 816, process 800 includes identifying a final set of genes. In some embodiments, the final set is identified according to the weighted F1 scores determined at each iteration of act 808 of process 800. For example, the set of genes that resulted in the highest weighted F1 score at act 808 may be selected.

At act 818, process 800 includes applying a rank transform to the expression values corresponding to the final set of genes identified at act 816. Techniques for ranking expression values are described above including at least with respect to act 806 of process 800 and with respect to FIG. 6A.

At act 820, the techniques include choosing the hyperparameters and fitting the statistical model. In some embodiments, this includes selecting the hyperparameters chosen at act 808 of process 800 that correspond to the final set of genes identified at act 816 of process 800.

In some embodiments, the final set of genes may correspond to the particular set of RNA genes for which RNA expression data should be obtained and processed using the trained machine learning classifier to determine whether to identify the molecular category as the candidate molecular category for the biological sample. Example RNA features corresponding to example molecular categories are provided in Table 3.

Training a DNA-Based Machine Learning Classifier

As described above, the machine learning techniques developed by the inventors include processing particular DNA features derived from DNA expression data using a particular machine learning classifier to determine whether to identify a particular molecular category as a candidate molecular category for the biological sample. Illustrative process 850 shows a flowchart for identifying the particular set of DNA features used for training a machine learning classifier, according to some embodiments of the technology described herein. Process 800 may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, computing device 104 as described herein with respect to FIG. 1A, computing device 1000 as described herein with respect to FIG. 10 , or in any other suitable way.

Process 850 begins with act 852 for obtaining genomic data. In some embodiments, the genomic data may be obtained using any suitable technique or combination of techniques, such as the techniques described herein including at least with respect to FIGS. 1A-B.

At act 854, process 850 includes deriving features from the genomic data. In some embodiments, the features include any feature or combination of features described above with respect to FIG. 1B, including, but not limited to, genes, mutations, mutational hotspots, tumor mutational burden, CNA genes, CNA values, LOH values ploidy, gene fusions, and MSI status.

At act 856, the techniques include choosing hyperparameters and fitting a statistical model. In some embodiments, this includes performing cross-validation using any suitable techniques, such as, stratified k-fold cross validation. For example, a 5-fold stratified cross-validation may be used. In some embodiments, any suitable train to test ratio may be used, such as, for example, 80 to 20 percent. Pedregosa et. al. (Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12(85): 2825-2830) describes an algorithm for realizing a stratified k-fold cross validation.

In some embodiments, the hyperparameters are selected according to a weighted F1 score of a cross-validation. Techniques for determining a weighted F1 score are described above including at least with respect to act 808 of process 800. Example hyperparameters include, but are not limited to number of estimators, number of leaver, learning rate, and share of features per one tree.

At act 858, process 850 includes calculating the importance each of features in the current set of features. This includes assigning a score to the feature based on how valuable it is in predicting the target variable. Gene importance can be calculated using any suitable method, as aspects of the technology described herein are not limited to any particular gene importance calculation technique. In some embodiments, regression coefficients may be used to determine gene importance (e.g., when using a linear regression classifier). In some embodiments, Gini importance may be used to determine gene importance (e.g., when using a gradient boosting classifier). In some embodiments. SHAP values may be used to determine gene importance (e.g., when using a gradient boosting tree classifier). For example, Lundberg et. al. (“From local explanations to global understanding with explainable AI for trees,” Nat Mach Intell 2, 56-57), which is incorporate herein by reference in its entirety, describes techniques for determining gene importance using SHAP values for gradient boosting tree classifiers,

At act 860, process 850 includes generating an updated set of the features by discarding at least a threshold number of the least important features, based on the calculated feature importances. For example, this may include discarding at least 1 feature, at least 2 features, at least 5 features, at least 8 features, at least 10 features, at least 15 features, at least 20 features, at least 25 features, between 1 and 30 features, between 2 and 15 features, between 2 and 5 features, or between 5 and 10 features. In some embodiments, the number of features discarded depends on the number of features included in the feature set. For example, more features with be discarded when the feature set is relatively large compared to the number of features discarded with the initial feature set is relatively small.

At act 862, process 850 includes determining whether the updated includes a minimum number of features. For example, the minimum number of features may include 0 features, at least 10 features, at least 20 features, at least 40 features, at least 60 features, at least 80 features, between 10 and 60 features, or between 20 and 40 features. In some embodiments, if the number of features in the updated set of features exceeds the minimum number of features, process 850 returns to act 856, where hyperparameters are chosen and a statistical model is fit. If there are no features remaining in the set, process 800 proceeds to act 864.

At act 864, process 850 includes identifying a final set of features. In some embodiments, the final set is identified according to the weighted F1 scores determined at each iteration of act 856 of process 850. For example, the set of features that resulted in the highest weighted F1 score at act 856 may be selected.

At act 866, the techniques include choosing the hyperparameters and fitting the statistical model. In some embodiments, this includes selecting the hyperparameters chosen at act 856 of process 850 that correspond to the final set of features identified at act 564 of process 500.

In some embodiments, the final set of features may correspond to the particular set of DNA features to be obtained from DNA expression data and processed using the trained machine learning classifier to determine whether to identify the molecular category as the candidate molecular category for the biological sample. Example DNA features corresponding to example molecular categories are provided in Table 5.

Molecular Category Identification Performance

FIG. 9A shows an example clustering of tumor samples in the space of gene expression. Each sample corresponds to a molecular category shown in the legend. Points corresponding to the same molecular category are shown to cluster together, indicating gene expression is a feature that may be useful for distinguishing between biological samples belonging to different molecular categories. Accordingly, the techniques described herein utilize gene expression data (e.g., RNA expression data) in identifying molecular categories for the biological samples.

FIG. 9B is a diagram illustrating the performance of the machine learning techniques developed by the inventors, according to some embodiments of the technology described herein. In particular, the diagram compares the molecular categories predicted according to the techniques developed by the inventors with the corresponding true molecular categories for the biological sample. As shown, the techniques perform with a 92.4% accuracy indicating that the techniques can be used to accurately and reliably identify a candidate molecular category for a biological sample, such as a tumor.

FIG. 9C is a diagram illustrating the performance of an RNA-based machine learning classifier developed by the inventors, according to some embodiments of the technology described herein.

FIG. 9D shows precision-recall curves illustrating the performance of the RNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9E shows receiver operating characteristic (ROC) curves illustrating performance of the RNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9F is a diagram illustrating the performance of a DNA-based machine learning classifier developed by the inventors, according to some embodiments of the technology described herein.

FIG. 9G shows precision-recall curves illustrating the performance of the DNA-based machine learning classifier, according to some embodiments of the technology described herein.

FIG. 9H shows receiver operating characteristic (ROC) curves illustrating performance of the DNA-based machine learning classifier, according to some embodiments of the technology described herein.

RNA and DNA Features

As described herein, in some embodiments, a machine learning classifier corresponding to a respective molecular category may be used to determine whether the molecular category is to be identified for a biological sample.

In some embodiments, the machine learning classifier for a particular molecular category may be an RNA-based machine learning classifier and may process, as input, features obtained from RNA expression data for a specific set of genes identified a priori for the particular molecular category.

Table 3 lists, for each of multiple different molecular categories, genes that are associated with the molecular category. In some embodiments, the techniques described herein include obtaining RNA expression data for at least some (e.g., at least ten, at least 15, at least 20, at least 25, at least 30, at least 45, at least 50, between 10 and 50, between 10 and 100) of the genes listed in Table 3 for a particular molecular category (e.g., the molecular categories listed in Table 2), obtaining RNA features from the expression data (e.g., gene rankings, expression levels, and/or any other suitable features) and processing the RNA features using an RNA-based machine learning classifier to determine whether to identify the particular molecular category as a candidate molecular for the biological sample.

Table 3 is divided into portions, where each portion includes genes that are listed for a molecular category. For example, the first portion includes genes listed for the molecular category “Gastrointestinal Adenocarcinoma.” For example, another portion includes genes listed for the molecular category “Pancreatic Adenocarcinoma.” For example, a third portion includes genes listed for the molecular category “Breast Cancer.”

In some embodiments, the machine learning classifier for a particular molecular category may be an DNA-based machine learning classifier and may process, as input, features obtained from DNA expression data for a specific set of features identified a priori for the particular molecular category.

Table 5 lists, for each of multiple different molecular categories, DNA features that are associated with the molecular category. In some embodiments, the techniques described herein include processing DNA expression data to obtain at least some (e.g., at least 10, at least 15, at least 20, at least 25, at least 30, at least 45, at least 50, between 10 and 50, between 10 and 100) of the DNA features listed in Table 5 for a particular molecular category (e.g., the molecular categories listed in Table 2) and processing the DNA features (e.g., mutational burden, normalized copy numbers etc.) using a DNA-based machine learning classifier to determine whether to identify the particular molecular category as a candidate molecular for the biological sample.

Table 5 is divided into portions, where each portion includes DNA features that are associated with a molecular category. For example, the first portion includes DNA features listed for the molecular category “Ovarian Cancer.” For example, another portion includes DNA features listed for the molecular category “Breast Cancer.” For example, another portion includes DNA features listed for the molecular category “Squamous Cell Carcinoma.” Table 4 lists descriptions of the DNA feature notation in Table 5 listed under column “DNA Feature.”

TABLE 3 Genes associated with molecular categories. Gene NCBI Gene ID NCBI Accession Number(s) Gastrointestinal_Adenocarcinoma TUSC3 7991 XM_011544651; XM_017013861; NM_178234; NM_006765; NM_001356429 ZG16 653808 NM_152338; XM_011545921 COLEC11 78989 XM_006711897; NM_001255986; NM_001255989; NM_001255985; NM_001255982; NM_001255983; NM_001255984; NM_024027; NR_045659; XM_005263853; NM_001255987; NM_001255988; NM_199235 KLF4 9314 NM_004235; NM_001314052 COBL 23242 XM_011515239; NM_015198; XM_011515236; XM_005271751; XM_011515237; NM_001287436; NM_001287438; NM_001346441; XM_011515235; XM_011515240; XM_017011898; NM_001346443; NM_001346444; XM_011515234; XM_011515241; NM_001346442; XM_005271750; XM_011515238 SIX1 6495 XM_017021602; NM_005982 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 EPHB2 2048 XM_006710441; NM_001309192; NM_004442; NM_001309193; NM_017449; XM_024453895; XM_006710442 CDH19 28513 XM_011525931.3; XM_017025711.2; XM_011525932.1 CDX1 1044 NM_001804 EN1 2019 NM_001426 CDH17 1015 NM_004063; XM_011516790; NM_001144663 WNT7A 7476 XM_011534091; NM_004625 SRD5A2 6716 XM_011533069; NM_000348; XM_011533072 ESM1 11082 NM_001135604; NM_007036 PRSS50 29122 NM_013270 PDX1 3651 NM_000209; XR_941580; XR_941578; BMP8A 353500 XM_017001198; XM_006710616; XM_011541381; XM_011541382; XR_946642; XR_946640; XR_946641; NM_181809 AGER 177 XR_001743190; NM_001206940; XM_017010328; NM_001206936; NM_001206954; NM_172197; XR_001743189; NM_001136; NM_001206929; NM_001206932; NM_001206934; NR_038190; NM_001206966 SYT12 91683 XM_011545346; XM_011545347; NM_177963; XM_017018547; NM_001177880; NM_001318775; XM_017018548; XM_006718737; XM_024448766; NM_001318773 CFD 1675 NM_001317335; NM_001928 GAMT 2593 NM_138924; NM_000156 VTCN1 79679 NM_001253849; NM_024626; NR_045604; XM_017002335; NM_001253850; NR_045603; XM_011542143 TMSB15A 11013 NM_021992 SLC15A2 6565 XM_006713736; XM_017007074; NM_021082; XM_005247722; NM_001145998 CP 1356 XM_006713500; XM_006713501; XM_017005735; XM_017005734; XM_006713499; XM_011512435; XR_427361; NM_000096; NR_046371 MAL 4118 NM_022438; NM_002371; NM_022440; NM_022439 KRT2 3849 NM_000423 IQCA1 79781 XM_017004960; NM_024726; NM_001270585; XM_011511865; XM_011511866; XM_011511864; NM_001270584; NR_073043 PVRL1 5818 NM_203285; NM_032767; NM_002855; NM_203286 PLA2G7 7941 NM_001168357; XR_001743639; XM_005249408; NM_005084; XR_002956305 STRA6 64220 NM_022369; NM_001199042; XM_011521883; XM_011521885; NM_001142618; XM_017022479; NM_001142617; NM_001142619; NM_001142620; XM_011521884; XR_931877; XM_017022478; XM_017022480; NM_001199040; NM_001199041 TREM2 54209 NM_001271821; NM_018965 ADAP1 11033 NM_001284308; NM_006869; NM_001284311; NM_001284310; NM_001284309 MUC13 56667 NM_033049 CLDN18 51208 NM_001002026; NM_016369 DPT 1805 NM_001937 PLP1 5354 NM_001128834; NM_000533; NM_001305004; NM_199478 CCNB1 891 NM_031966 GPR162 27239 NM_014449; NM_019858 ONECUT2 9480 NM_004852 SFTPD 6441 XM_011540087; NM_003019; XM_011540088 CLDN10 9071 XM_024449432; XM_017020844; NM_006984; XM_011521134; XM_017020843; NM_182848; NM_001160100 NXPH4 11247 XM_017018747; NM_007224 MAB21L2 10586 NM_006439 REG3A 5068 NM_138938; NM_002580; NM_138937 LGALS4 3960 NM_006149; XM_011526974; XM_011526973 GPR35 2859 NM_001195382; NM_001195381; NM_001394730; NM_005301 HIF3A 64344 XM_017027133; XM_017027139; XM_024451649; XR_001753736; XR_935849; NM_022462; XM_017027132; XM_017027142; XM_005259152; XM_017027138; NM_152796; XM_005259156; XM_005259155; XM_017027136; XM_017027137; XR_002958343; XM_005259153; XM_017027135; XM_017027140; NM_152794; XM_017027134; XM_017027141; NM_152795 SIM2 6493 XM_017028442; XR_001754891; XM_011529694; NM_005069; NM_009586 TCF21 6943 NM_003206; NM_198392 SCTR 6344 XM_005263730; XR_001738888; XR_922984; XM_017004672; XM_011511621; XM_017004673; XM_024453038; XM_017004670; XR_001738887; XR_001738889; XM_017004671; NM_002980 CCL11 6356 NM_002986 SLC34A2 10568 NM_001177999; NM_006424; NM_001177998 GIF 2694 XM_011544939; NM_005142 SALL1 6299 NM_001127892; NM_002968 HGH1 51236 NM_016458; XR_001745537 KCNC3 3748 NM_004977; NR_110912; NM_001372305 GPA33 10223 XM_017000005; NM_005814 SLC6A13 6540 XM_006719008; XM_011521012; XM_017019842; XM_017019845; XM_017019846; NM_016615; XM_017019847; NM_001190997; XM_011521013; XM_017019844; XR_001748849; XR_002957372; NM_001243392 FXYD2 486 NM_021603; NM_001127489; NM_001680 HNF4A 3172 XM_005260407; NM_001287182; NM_001030003; NM_178850; NM_175914; NM_001030004; NM_178849; NM_001258355; NM_001287183; NM_000457; NM_001287184 GABRQ 55879 NM_018558; XM_011531184 ABCA4 24 NM_000350 MMP11 4320 NM_005940; NR_133013 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 INHBA 3624 XM_017012175; NM_002192; XM_017012176; XM_017012174 REG1A 5967 NM_002909 TSPYL2 64061 XM_006724592; XM_017029727; NM_022117; XR_001755719; XM_017029726 ERBB4 2066 XM_005246376; XM_017003577; XM_017003578; XM_005246377; NM_001042599; XM_017003581; XM_006712364; XM_017003582; XM_017003579; XM_017003580; NM_005235 LRRC15 131578 NM_130830; NM_001135057 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 INS 3630 NM_001185098; NM_001185097; NM_000207; NM_001291897 FABP4 2167 NM_001442 NELL2 4753 XM_017019343; XM_017019344; NM_001145107; XM_011538396; NM_001145109; XM_017019341; NM_001145110; XM_017019342; NM_006159; XM_005268905; NM_001145108 CST1 1469 NM_001898 TM4SF5 9032 NM_003963 PODXL 5420 NM_005397; NM_001018111 CRNN 49860 NM_016190 WISP2 8839 NM_001323369; XM_017028116; NM_003881; XM_017028117; NM_001323370 SST 6750 NM_001048 LIN37 55957 NR_163146; NM_019104; NM_001369780 GREM1 26585 NM_001368719; NM_013372; NM_001191323; NM_001191322 SLCO1A2 6579 NM_001386879; NM_001386886; NM_001386908; NM_001386920; NM_001386926; NM_001386939; NM_001386959; NM_001386960; XM_011520819; NM_001386881; NM_001386929; NM_134431; NR_170340; NM_001386878; NM_001386946; NM_001386952; XM_024449138; NM_001386890; NM_001386922; NM_001386938; NM_001386947; NM_001386961; XM_011520821; NM_001386927; NM_001386940; NM_001386948; NM_001386949; NM_001386958; NM_001386880; NM_001386882; NM_001386937; NM_001386951; NM_001386962; NM_001386963; NM_001386887; NM_001386921; NM_001386954; NR_170341; NR_170343; NM_005075; XM_017019849; NM_001386919; NM_001386931; NM_001386953; NM_021094 GRIN2D 2906 XM_011526872; NM_000836 APOC1 341 NM_001645; NM_001321066; NM_001379687; NM_001321065 GDPD3 79153 NM_024307 FOXF1 2294 NM_001451 TGFB3 7043 NM_001329938; NM_003239; NM_001329939 ST3GAL5 8869 NM_001354248; XM_017005208; XM_017005214; NM_001354226; XM_017005204; NM_001354233; NM_001354234; XM_017005205; XM_017005213; XR_001739019; NM_003896; NM_001354223; NM_001354227; NM_001354247; XM_017005206; XR_001739021; NM_001042437; NM_001354229; XM_017005202; XM_017005203; XM_017005212; XR_001739020; XM_017005209; NM_001354224; NM_001363847; NM_001354238 DIRAS2 54769 NM_017594 GABRG3 2567 XM_017022058; XM_017022060; XM_024449889; NM_033223; XM_011521430; NM_001270873; XM_011521431; XM_017022059 HOXC11 3227 NM_014212 RAPGEF3 10411 XM_011537758; XM_024448795; XR_001748551; XR_002957282; NM_001098532; XM_005268571; XM_017018688; NM_001098531; XM_011537752; XR_001748550; NM_006105; XM_011537755 SLCO4A1 28231 XR_002958473; XR_001754251; XR_001754254; XR_001754255; XR_001754258; NM_016354; XR_001754250; XR_244116; XM_017027827; XR_001754253; XR_001754252; XR_244115; XR_936524; XM_017027826; XR_002958474; XR_001754256; XR_001754257; XM_005260203; XM_011528792; XR_001754249 FABP1 2168 NM_001443 NFE2L3 9603 NM_004289 GLRB 2743 XR_001741207; XM_017008035; NM_000824; NM_001166060; XR_002959723; XM_017008034; NM_001166061 PTH1R 5745 NM_001184744; XM_017006933; XM_011533968; NM_000316; XM_017006934; XM_011533967; XM_005265344; XM_017006932 C2orf72 257407 NM_001144994 CAPN3 825 NM_173087; NM_173089; NM_024344; NM_173088; NM_212465; NR_027912; NM_000070; NM_173090; NR_027911 SLC2A4 6517 NM_001042 MLF1 4291 NM_001369782; NM_001369785; NM_001378847; NM_022443; NM_001378845; NM_001378848; NM_001378851; NM_001369784; NM_001378853; NM_001378855; NM_001130156; NM_001369783; NM_001378852; NM_001130157; NM_001195432; NM_001195433; NM_001378846; NM_001378850; NM_001369781; NM_001195434 FEZF2 55079 NM_018008 APCS 325 NM_001639 SOX9 6662 NM_000346 HOXC10 3226 NM_017409 PKNOX2 63876 NR_168078; NM_001382330; NM_001382335; NR_168084; NM_001382328; NM_001382329; NM_001382341; NR_168083; NM_022062; NM_001382324; NM_001382326; NM_001382334; NM_001382336; NM_001382337; NM_001382340; NR_168079; NR_168080; NR_168081; NM_001382325; NM_001382323; NM_001382327; NM_001382332; NM_001382338; NM_001382339; NR_168076; NR_168077; NM_001382331; NM_001382333; NR_168082 DNAI1 27019 NM_012144; NM_001281428 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 CDX2 1045 XM_011534876; NM_001354700; XM_011534879; XM_011534875; XM_011534878; NM_001265 TNNT2 7139 XM_011509943; NM_001001430; XM_011509946; XM_017002217; XM_011509941; XM_024449450; XM_024449455; NM_001001432; XM_006711508; XM_011509939; XM_017002216; XM_006711509; XM_011509942; NM_000364; NM_001276346; NM_001276347; XM_011509944; NM_001001431; XM_011509938; XM_011509940; XM_024449454; NM_001276345 ADH1B 125 NM_001286650; NM_000668 EPS8L3 79574 XM_017002329; XM_011542135; XM_011542134; NM_139053; NM_001319952; NM_024526; XM_011542133; XM_017002328; XR_001737407; XM_017002327; NM_133181; XM_011542132; XR_001737406 CHST2 9435 NM_004267 FGGY 55277 XM_017001645; XM_017001677; XM_024448207; XM_024448220; NM_001350792; NM_001350797; NM_001350798; NM_018291; XM_011541731; XM_017001671; XM_017001673; NM_001244714; NM_001350793; NM_001350794; NR_103473; XM_011541730; XM_017001649; XM_017001670; XM_017001678; XM_024448227; NM_001113411; XM_017001643; XM_011541736; XM_017001659; XM_017001662; XM_017001664; XM_024448185; XR_001737287; NM_001350791; NM_001350796; XM_017001668; XM_017001679; XR_001737285; XM_017001646; XM_017001652; XM_024448176; XR_001737286; NM_001278224; XM_017001657; XM_017001660; XR_001737284; NM_001350790; NM_001350799; XM_017001655; XM_017001656; XM_017001661; XM_017001663; XM_017001669; XM_024448196; XM_024448229; NM_001350795 FERMT1 55612 NM_017671; XM_024451935 PRSS3 5646 NM_007343; NM_001197097; NM_002771; XM_011517965; NM_001197098 CCNA1 8900 XM_011535294; XM_011535296; NM_001111047; XM_011535295; NM_001111046; NM_003914; NM_001111045 ARL4D 379 XM_011524782; NM_001661 LZTS1 11178 XM_011544386; XM_011544384; NM_021020; NM_001362884; XM_011544385 RAP1GAP 5909 XR_001737354; XR_001737351; NM_001145657; NM_001350527; NM_001350528; NM_001388217; NM_001388229; NM_001388241; NM_001388254; NM_001388259; NM_001388263; NM_001388266; NM_001388267; NM_001388276; NM_001388285; NM_001388287; NM_001388290; NM_001388294; NM_001388295; NR_170904; NR_170911; NR_170915; NR_170920; NR_170928; XR_001737352; XR_946730; NM_001145658; NM_001330383; NM_001388205; NM_001388211; NM_001388216; NM_001388221; NM_001388224; NM_001388227; NM_001388239; NM_001388245; NM_001388280; NM_001388281; NR_170900; NR_170923; NR_170927; NM_001350526; NM_001388222; NM_001388243; NM_001388252; NM_001388256; NM_001388258; NM_001388261; XR_946728; NM_001388203; NM_001388209; NM_001388206; NM_001388230; NM_001388231; NM_001388240; NM_001388242; NM_001388247; NM_001388253; NM_001388255; NM_001388288; NM_001388289; NM_001388296; NR_170907; NR_170909; XR_001737349; NM_001350525; NM_001388204; NM_001388207; NM_001388210; NM_001388219; NM_001388220; NM_001388228; NM_001388233; NM_001388235; NM_001388236; NM_001388238; NM_001388248; NM_001388284; NM_001388286; NR_170910; NR_170924; NM_001388202; NM_001388208; NM_001388214; NM_001388218; NM_001388234; NM_001388249; NM_001388270; NM_001388279; NM_002885; NR_170901; NR_170902; NR_170903; NR_170912; NR_170913; NR_170926; XR_946726; NM_001350524; NM_001388200; NM_001388212; NM_001388213; NM_001388215; NM_001388225; NM_001388226; NM_001388244; NM_001388246; NM_001388251; NM_001388282; NM_001388283; NR_170908; NR_170914; NR_170921; NR_170925; NM_001388201; NM_001388223; NM_001388237; NM_001388250; NM_001388264; NM_001388269; NM_001388273; NM_001388291; NM_001388292; NM_001388293 KRT24 192666 XM_017024299; NM_019016; XM_006721739; XM_011524460 SCNN1D 6339 NM_001130413; NR_037668; NM_002978 ZBTB20 26137 NM_001164345; NR_121662; NM_001164347; NM_001348803; NM_001164343; NM_001393393; NM_001164342; NM_001348800; NM_001348801; NM_001348804; NM_001393395; NM_001393396; NM_001164344; NM_001348802; NM_001348805; NM_001393394; NM_001164346; NM_015642 AQP4 361 NM_001317387; NM_001364287; NM_001364286; NM_001317384; XM_011525942; NM_001650; NM_001364289; NM_004028 MUC2 4583 NM_002457 FGF23 8074 NM_020638 CXCL3 2921 NM_002090 IGFBP3 3486 NM_000598; NM_001013398 GABRA2 2555 XM_024453964; NM_001330690; NM_001377144; NM_001377149; XM_024453966; NM_001377150; XM_011513675; NM_001114175; NM_001377155; NM_000807; NM_001377147; XM_024453967; NM_001377146; NM_001377152; NM_001286827; NM_001377153; NM_001377145; NM_001377148; NM_001377151; NM_001377154 HR 55806 XM_006716367; NM_005144; XM_005273569; NM_018411 AKR1C2 1646 NM_001354; NM_001321027; NM_001135241; NM_205845; NM_001393392 MYOC 4653 NM_000261 TACR2 6865 NM_001057 VIP 7432 XM_006715562; XM_005267135; NM_003381; NM_194435 PRM2 5620 NM_001286358; NR_104428; NM_002762; NM_001286356; NM_001286359; NM_001286357 ACADL 33 NM_001608; XM_005246517; XM_017003955 SLC47A1 55244 NM_018242 CLPB 81570 NM_030813; XM_005274320; XM_011545289; NM_001258392; NM_001258393; NM_001258394 SCNN1B 6338 XM_017023526; XM_011545913; XM_011545914; XM_017023525; NM_000336 GLP2R 9340 XM_011524077; NM_004246; XM_017025340; XM_005256861; XM_017025339; XM_017025341 CASR 846 XM_017007325; NM_000388; XM_005247837; XM_017007324; NM_001178065; XM_006713789 IFI6 2537 NM_002038; XM_024446207; NM_022873; NM_022872 Pancreatic_Adenocarcinoma PNLIP 5406 NM_000936 PPY 5539 NM_002722; NM_001319209; XM_011524978 CTRC 11330 XM_011540550; NM_007272 CTRB2 440387 NM_001025200 CRP 1401 NM_000567; NM_001329058; NM_001382703; NM_001329057 GCG 2641 NM_002054 PNLIPRP1 5407 XM_011539869; NM_001303135; NM_006229; XR_945774 INS 3630 NM_001185098; NM_001185097; NM_000207; NM_001291897 CPA1 1357 NM_001868 CASR 846 XM_017007325; NM_000388; XM_005247837; XM_017007324; NM_001178065; XM_006713789 GCNT3 9245 NM_004751 TFF2 7032 NM_005423 PDX1 3651 NM_000209; XR_941580; XR_941578; SCTR 6344 XM_005263730; XR_001738888; XR_922984; XM_017004672; XM_011511621; XM_017004673; XM_024453038; XM_017004670; XR_001738887; XR_001738889; XM_017004671; NM_002980 ALPPL2 251 NM_031313 PADI1 29943 XM_017001102; XR_946617; XR_946619; NM_013358; XR_001737131; XM_011541307; XR_001737130; XM_017001103; XR_946620; XM_017001101 CTSE 1510 XM_011509245; NM_001910; NM_148964; XM_011509244; NM_001317331 FOXL1 2300 NM_005250 LHX2 9355 NM_004789; XM_006717323 POU3F3 5455 NM_006236 MIA 8190 NM_006533; NM_001202553 HOXD13 3239 XM_011511068; NM_000523; XM_011511069 NMRK2 27231 NM_001289117; NM_001375468; NM_001375469; NM_170678; NM_001375467; NM_014446; XM_006722725; NR_110316 TMPRSS4 56649 XM_011542901; NM_001290094; XM_005271614; NM_001173552; NM_183247; NR_110734; XM_005271613; XM_011542902; XM_011542904; XM_005271615; NM_001083947; NM_001173551; NM_019894; XM_011542903; NM_001290096 HAND2 9464 NM_021973 IHH 3549 NM_002181 MAGEA3 4102 XM_011531161; XM_005274676; XM_006724818; XM_011531160; NM_005362 KLK6 5653 XM_024451611; NM_001319949; NM_001012964; NM_001319948; NM_001012965; NM_002774 PRAME 23532 XM_011530034; NM_206954; NM_001318126; NM_001318127; NM_001291715; NM_001291719; NM_001291716; NM_006115; NM_001291717; NM_206953; NM_206956; NM_206955 MAGEA6 4105 NM_175868; NM_005363 LRP2 4036 XM_011511183; NM_004525; XM_011511184 MYBPH 4608 NM_004997 CR2 1380 NM_001877; NM_001006658; XM_011509206 GABRA3 2556 NM_000808; XM_006724811 MYH7 4625 XM_017021340; NM_000257 ENPP3 5169 XR_001743464; NR_133007; NM_005021; XM_017010932; XM_011535897 GABRQ 55879 NM_018558; XM_011531184 NXPH4 11247 XM_017018747; NM_007224 FOXA1 3169 NM_004496; XM_017021246 SFTPB 6439 XM_005264487; NM_198843; XM_005264488; NM_000542; NM_001367281; XM_005264490 DLX6 1750 NM_005222 CRNN 49860 NM_016190 HOXA7 3204 NM_006896 NEFM 4741 NM_001105541; NM_005382 KRT24 192666 XM_017024299; NM_019016; XM_006721739; XM_011524460 FCER2 2208 NM_002002; NM_001220500; XM_005272462; NM_001207019 CLDN3 1365 NM_001306 POU2F2 5452 XM_017026886; XM_017026889; XM_017026895; XR_001753709; XR_001753710; NM_001393935; XM_017026885; XM_017026891; XM_017026894; XM_024451547; NM_001207026; NM_001393934; NM_001394376; NM_001394378; XM_017026884; XM_011527043; XM_017026887; XM_017026890; NM_001247994; XM_011527041; XM_024451546; NM_001207025; XM_011527042; XM_017026888; XM_017026892; NM_001393936; NM_002698; XM_017026896; NM_001394377 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 BCL11A 53335 NM_001365609; NM_022893; NM_138553; XM_017004335; XM_024452962; XM_024452963; XM_017004333; NM_138559; XM_011532910; XM_017004336; NM_018014; XM_011532909; NM_001363864 CX3CR1 1524 NM_001171174; NM_001337; NM_001171171; NM_001171172 ABCA12 26154 XM_011510951; NR_103740; NM_173076; NM_015657 Breast_Cancer AMN 81693 XM_024449714; XM_011537203; NM_030943; XM_011537202 NMRK2 27231 NM_001289117; NM_001375468; NM_001375469; NM_170678; NM_001375467; NM_014446; XM_006722725; NR_110316 TLX2 3196 NM_001534; NM_016170 MYH15 22989 XM_011512559; NM_014981; XM_017005922 MROH7 374977 NR_026782; NM_198547; NM_001039464; NM_001291332; NR_111931 ERN2 10595 XM_011545708; XM_011545711; XR_950727; XM_011545709; XM_011545712; NM_001308220; XM_011545713; NM_033266 CSF3 1440 NR_168489; NR_168491; NM_000759; NM_172220; NM_001178147; NM_172219; NR_168490; NR_033662 TMEM246 84302 NM_001303107; NM_001303108; NM_032342; XM_024447701; NM_001371233 GCGR 2642 XM_011523539; XM_017024446; NM_000160; XM_006722277; XM_017024447 NEFM 4741 NM_001105541; NM_005382 SOX21 11166 NM_007084 PMP2 5375 NM_002677; NM_001348381 RGS20 8601 NM_001286673; NM_001286675; NM_170587; NM_001286674; NM_003702; NR_104578; NR_104579 IL13RA2 3598 NM_000640 GPR17 2840 NM_005291; NM_001161416; NM_001161415; XM_017003833; NM_001161417 B3GALT1 8708 NM_020981; XM_006712819; XM_011512085 MT1H 4496 NM_005951 GJA3 2700 NM_021954; XM_011535048 SCTR 6344 XM_005263730; XR_001738888; XR_922984; XM_017004672; XM_011511621; XM_017004673; XM_024453038; XM_017004670; XR_001738887; XR_001738889; XM_017004671; NM_002980 DBH 1621 NM_000787 OGDHL 55753 XM_011539946; NM_001347821; NM_001143997; NM_001347820; NM_001347823; NR_144685; XM_017016402; NM_001347819; NM_001347825; NM_018245; NR_144682; NM_001347824; NR_144683; XM_017016403; NM_001143996; NM_001347822; NM_001347826; NR_144684; NR_144686 WNT7A 7476 XM_011534091; NM_004625 RPRM 56475 NM_019845 CA4 762 XM_017025012; XR_001752604; NM_000717; XM_005257639; XR_001752608; NR_137422; XR_001752605; XR_001752607; XR_001752610; XM_011525183; XR_001752606; XR_001752609 FOXA2 3170 NM_021784; NM_153675 ZNF536 9745 XM_011527557; XM_017027530; XM_017027533; XM_017027534; XM_017027540; XM_017027535; XM_017027531; XM_017027532; XM_017027539; XM_017027542; XM_011527555; XM_011527560; XM_017027536; NM_001352260; NM_001376110; NM_014717; XM_011527554; XM_017027527; XM_017027537; XM_017027543; XM_024451807; NM_001376111; XM_011527558; XM_017027528; XM_017027529; XM_017027538 CCL16 6360 NM_004590; XM_005258020 SHH 6469 NR_132319; NM_000193; NR_132318; XM_011516480; XM_011516479; NM_001310462 TAC3 6866 NR_135164; NR_135166; NR_135165; NM_001006667; NM_001178054; NM_013251; NR_033654 CXCL3 2921 NM_002090 DUSP26 78986 NM_024025; NM_001305116; NM_001305115 SERPIND1 3053 NM_000185 SLC6A13 6540 XM_006719008; XM_011521012; XM_017019842; XM_017019845; XM_017019846; NM_016615; XM_017019847; NM_001190997; XM_011521013; XM_017019844; XR_001748849; XR_002957372; NM_001243392 TCF21 6943 NM_003206; NM_198392 TYR 7299 XM_011542970; NM_000372 DUOX2 50506 NM_014080; NM_001363711 SLC45A2 51151 NM_001297417; NM_016180; NM_001012509 MAB21L2 10586 NM_006439 GAS2 2620 NM_001143830; NM_001391933; NM_001391935; NM_001391936; XM_011519972; NM_001391937; NM_001391934; XM_011519971; NR_147085; XM_017017532; XR_001747829; NM_001351224; XM_011519975; NM_005256; NM_177553 IL1A 3552 NM_001371554; NM_000575 SPRR2B 6701 NM_001388198; NM_001017418 CYP2W1 54905 NM_017781; XM_011515440; XM_011515441 SPOCK3 50859 NM_001251967; NM_001204354; NM_001204356; XM_011532018; NM_001204359; XM_017008258; NM_001040159; NM_001204358; XM_017008257; NM_001204352; NM_016950; NM_001204353; NM_001204355 KCNK12 56660 NM_022055 HKDC1 80201 NM_025130; XR_001747209; XM_011540195 HNF1B 6928 XM_011525161; NM_001165923; NM_001304286; XM_011525163; NM_000458; XM_011525162; NM_006481; XM_011525164; XM_011525160 MASP1 5648 XM_011512989; XM_017006869; XM_017006870; XM_017006871; NM_001031849; XM_006713701; XM_011512990; NM_001879; NR_033519; XM_017006872; XM_011512991; NM_139125 FOXE1 2304 NM_004473 NR1H4 9971 NR_135146; XM_006719719; NM_001206978; NM_001206993; NM_001206977; XM_011539040; XM_011539042; NM_001206979; NM_005123; XM_011539041; NM_001206992 NAALAD2 10003 XM_017017044; XR_001747709; XM_017017043; XR_001747707; XR_001747710; XR_001747711; NM_001300930; XR_001747708; XM_017017045; XM_017017046; NM_005467 HMGA2 8091 NM_001015886; NM_003483; NM_001300918; NM_003484; NM_001330190; NM_001300919 FOXF1 2294 NM_001451 RXRG 6258 NM_006917; NM_001256570; NM_001256571; NR_033824 NLGN4Y 22829 XM_011531429; NM_001365586; XM_017030036; NM_001365591; XM_006724874; XM_011531427; XM_011531428; XM_017030041; NM_001164238; NM_001206850; NR_028319; XM_017030039; NR_046355; NM_014893; XM_011531430; NM_001365588; NM_001365592; NM_001394830; XM_017030040; NM_001365584; NM_001365590; XM_024452490; NM_001365593; NM_001394831 DDX3Y 8653 NR_136716; NR_136718; NR_136719; NR_136721; NM_001122665; NR_136720; NR_136723; NM_004660; NM_001324195; XR_001756014; NM_001302552; NR_136717; NR_136724; NR_136722 EIF1AY 9086 NM_004681; NM_001278612 KDM5D 8284 XM_005262561; XR_002958832; XR_002958834; XR_002958837; XR_244571; NM_001146705; XM_011531468; XR_001756013; XM_024452495; XM_005262560; XM_024452496; XR_001756009; XR_001756011; XR_002958835; XR_001756010; NM_001146706; XR_002958836; XR_430568; NM_004653; XR_001756012; XR_002958833 STXBP6 29091 XM_017021235; NM_001351941; NM_001394415; XM_024449547; NM_001304476; NM_001351942; NM_001394413; XM_006720121; NM_001304477; NM_001394414; NM_001394417; XM_017021232; NM_014178; NM_001394410; NM_001394411; NM_001394420; XM_017021241; NM_001351943; NM_001394418; NM_001351940; NM_001394412; NM_001394416; NM_001394419 UTY 7404 XM_011531453; XM_011531464; XM_017030066; XM_017030067; NM_001258252; NM_001258260; NM_001258261; NM_001258270; NM_182659; NR_047597; NR_047618; NR_047621; XM_011531465; XM_024452493; NM_001258249; NM_001258251; NM_001258268; NR_047598; NR_047600; NR_047615; NR_047640; XM_006724875; XM_011531451; NM_001258269; NM_007125; NM_182660; NR_047606; NR_047616; NR_047620; NR_047631; NR_047639; NR_047641; NR_047647; XM_005262518; XM_011531454; XM_011531458; XM_011531459; XM_011531462; XM_017030073; XR_002958831; NM_001258257; NM_001258263; NM_001258266; NR_047601; NR_047611; NR_047613; NR_047619; NR_047627; NR_047634; NR_047645; NR_047646; XM_011531460; XM_011531461; XM_017030070; NM_001258256; NM_001258262; NM_001258264; NM_001258265; NR_047607; NR_047612; NR_047617; NR_047625; NR_047629; NR_047636; NR_047643; XM_011531442; XM_011531447; XM_011531450; XM_011531452; XM_017030074; XR_001756008; NM_001258253; NM_001258258; NM_001258259; NM_001258267; NR_047596; NR_047603; NR_047608; NR_047609; NR_047610; NR_047614; NR_047622; NR_047623; NR_047628; NR_047637; NR_047644; XM_011531448; XM_011531449; XM_017030068; XM_017030072; XM_024452494; NM_001258250; NR_047599; NR_047602; NR_047604; NR_047605; NR_047624; NR_047630; NR_047638; XM_011531441; XM_011531443; XM_011531445; XM_011531446; XM_011531455; XM_011531463; XM_017030071; NM_001258254; NM_001258255; NR_047626; NR_047635; NR_047632; NR_047633; NR_047642 RPS4Y1 6192 NM_001008 PKNOX2 63876 NR_168078; NM_001382330; NM_001382335; NR_168084; NM_001382328; NM_001382329; NM_001382341; NR_168083; NM_022062; NM_001382324; NM_001382326; NM_001382334; NM_001382336; NM_001382337; NM_001382340; NR_168079; NR_168080; NR_168081; NM_001382325; NM_001382323; NM_001382327; NM_001382332; NM_001382338; NM_001382339; NR_168076; NR_168077; NM_001382331; NM_001382333; NR_168082 GFAP 2670 XM_024450691; XM_024450690; NM_001131019; XM_024450692; XM_024450693; NM_001242376; NM_002055; NM_001363846 HIF3A 64344 XM_017027133; XM_017027139; XM_024451649; XR_001753736; XR_935849; NM_022462; XM_017027132; XM_017027142; XM_005259152; XM_017027138; NM_152796; XM_005259156; XM_005259155; XM_017027136; XM_017027137; XR_002958343; XM_005259153; XM_017027135; XM_017027140; NM_152794; XM_017027134; XM_017027141; NM_152795 PVRL3 25945 XM_011512663; XM_017006126; NM_001243286; XR_924122; NM_015480; XR_002959508; XM_017006125; XM_017006124; XM_017006127; XM_017006123; NM_001243288 SERPINB13 5275 NM_001348267; XM_011526029; NM_001348268; NM_012397; NM_001348269; NM_001307923; NM_001348270 ADH1C 126 NM_000669; NR_133005 EYA4 2070 XM_005266851; NM_004100; NM_172105; NM_001370459; NM_172104; XM_017010371; XR_001743220; NM_001301012; XM_017010369; XM_017010370; XM_017010372; XM_017010373; XR_001743219; NM_172103; NM_001301013; NM_001370458; XM_017010368; XM_017010374 RGS6 9628 XM_017021825; XM_017021832; XM_024449763; XR_001750613; NM_001370274; NM_001370279; NM_001370284; NM_001370291; XM_017021820; XM_024449761; XM_024449770; XM_024449774; NM_001370272; NM_001370277; NM_001370278; NM_001370292; XM_011537397; XM_017021831; XM_024449764; NM_001204421; NM_001204423; NM_001370275; NM_001370290; NM_001370293; NR_135235; XM_024449760; XM_024449776; XR_002957573; NM_001204416; NM_001204417; NM_001370271; NM_001370283; NM_001370270; NM_001370273; NM_001370281; NM_001370286; XM_017021822; XM_017021833; NM_001204422; NM_001204424; NM_001370276; NM_001370280; NM_001370287; NM_001370289; NM_001370294; XM_011537393; XM_011537407; XM_017021827; XM_017021830; XM_017021834; XM_024449759; NM_001370282; XM_017021826; XM_017021828; XM_024449768; NM_001204418; NM_001204419; NM_001204420; NM_001370288; NM_004296 ACTC1 70 NM_005159 PAX3 5077 NM_181457; NM_000438; NM_181459; NM_181460; NM_001127366; NM_013942; NM_181461; NM_181458 GALNT12 79695 XM_006717287; XM_017015133; XM_011519018; NM_024642; XM_011519020; XM_024447673 SOX2 6657 NM_003106 SNCA 6622 XM_011532204; NM_001146054; NM_000345; NM_001375287; XM_011532206; NM_007308; NR_164675; XM_011532207; NM_001375288; NM_001375290; NR_164676; XM_011532203; XM_011532205; NR_164674; XM_017008563; NM_001146055; NM_001375286; NM_001375285 MYLPF 29895 NM_001324458; NM_013292; NM_001324459 EMX2 2018 NM_004098; NM_001165924 FRMPD1 22844 XM_017014482; XM_024447456; XM_011517806; NM_001371223; NM_001371225; XM_017014481; XM_024447454; XM_011517805; XR_929220; NM_014907; NM_001371224 PHYHIP 9796 NR_156475; NM_001099335; NM_001363311; NM_014759; XM_017014102; NM_001363312 GUCY2C 2984 NM_004963; XM_011520631 FGFBP1 9982 NM_005130 SGK2 10110 NM_016276; NM_001199264; NM_170693 GDF10 2662 NM_004962 REM1 28954 XM_011528795; XM_017027833; NM_014012; XM_005260404 CPEB1 64506 NM_001288819; NM_001365243; NM_001365242; NM_001365244; NM_001365245; NM_001387068; NM_001387076; NM_001365248; NM_001079534; NM_001365250; NM_001387065; NM_001387075; NM_001079535; NM_001288820; NM_001365249; NM_001387061; NM_001387066; NM_001387070; NM_001387062; NM_001387071; NM_001387078; NM_001365246; NM_001365247; NM_001387069; NM_001387077; NM_001079533; NM_001365240; NM_001365241; NM_001387072; NM_001387074; NM_001387063; NM_001387064; NM_001387067; NM_001387073; NM_030594 CYP3A5 1577 NM_001291830; NM_001190484; NR_033807; NR_033812; NM_001291829; NM_000777; NR_033810; NR_033811 SALL1 6299 NM_001127892; NM_002968 HAND2 9464 NM_021973 HOXA3 3200 NM_001384342; NM_001384335; NM_001384336; NM_001384339; NM_001384345; NM_001384346; NM_001384338; NM_001384337; NM_030661; NM_001384341; NM_001384343; NM_001384340; NM_001384344; NM_153631; NM_153632 TMPRSS5 80975 XM_017018366; XR_001747990; NM_001288749; NM_001288751; NM_001288752; NM_001288750; NR_110047; XR_001747991; XR_001747992; NR_110046; NM_030770; XM_017018367 BMP5 653 XM_011514817; NM_001329756; XM_024446524; NM_001329754; NM_021073 TRDN 10345 NM_001251987; NM_001256020; NM_001256021; NM_006073; NM_001256022 TACR2 6865 NM_001057 LYVE1 10894 NM_006691 FHL1 2273 NM_001159703; NM_001167819; NM_001369327; NM_001369330; XM_006724746; XM_024452354; NR_027621; NM_001369328; NM_001159702; NM_001369326; XM_006724743; NM_001330659; NM_001369331; NM_001159700; NM_001159701; NM_001159704; NM_001369329; NM_001159699; NM_001449 CAV1 857 NM_001753; NM_001172895; NM_001172897; NM_001172896 FIGF 2277 NM_004469 NPR1 4881 XM_017001374; XM_005245218; NM_000906 SORBS1 10580 XM_017015501; XM_017015503; XM_017015510; XM_017015511; XM_017015512; XM_017015539; NM_001034957; NM_001290296; NM_001290297; NM_001290298; NM_001377208; NM_001377209; NM_001384448; NM_001384453; NM_001384456; NM_001384461; XM_006717589; XM_011539155; XM_017015500; XM_017015505; XM_017015509; XM_024447770; NM_001290294; NM_001384450; NM_001384460; NM_015385; NM_024991; XM_011539150; XM_017015506; XM_017015536; XM_024447769; NM_001377206; NM_001384452; NM_001384459; NM_001384463; XM_011539167; XM_017015514; XM_017015515; NM_001290295; NM_001377200; NM_001377207; NM_001384455; NM_001384464; XM_017015504; NM_001034954; NM_001034955; NM_001377201; NM_001384447; NM_001384449; NM_001384457; NM_001384458; NM_006434; XM_011539140; XM_017015502; XM_017015513; XM_017015523; XM_017015525; XM_017015537; XM_017015540; NM_001034956; NM_001377198; NM_001377205; NM_001384462; XM_017015507; XM_017015508; XM_017015517; XM_017015530; XM_017015532; XM_017015533; NM_001377199; NM_001377203; NM_001377204; NM_001384451; NM_001384454; NM_001384465; NM_001377197; NM_001377202 AOC3 8639 XR_934584; NM_001277732; NM_003734; NR_102422; XM_011525419; XR_001752673; XM_011525420; XM_024451015; NM_001277731 KCNIP2 30819 XM_006717812; NM_173342; XM_005269729; XM_005269730; NM_014591; NM_173197; XM_011539731; NM_173191; NM_173195; XM_017016161; NM_173192; NM_173194; NM_173193 CIDEC 63924 NM_001321142; NM_001199552; NM_001378491; NM_001199623; NM_001199551; NM_001321144; NM_022094; NM_001321143 NEK2 4751 NM_002497; NM_001204182; NM_001204183 MMP11 4320 NM_005940; NR_133013 ADAMTS5 11096 XM_024452053; XM_024452054; NM_007038 ABCD2 225 XR_001748623; NM_005164; XM_017018992; XR_001748622; XM_017018993; XM_011538027 LPL 4023 NM_000237 HBB 3043 NM_000518 PPARG 5468 NM_001354669; NM_001354670; NM_001374263; NM_001330615; NM_001374262; NM_005037; NM_001374261; NM_138711; NM_138712; NM_001374264; NM_001374266; NM_001354668; NM_015869; NM_001354667; NM_001354666; NM_001374265 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 AQP7 364 XM_006716765; XM_017014706; NM_001318158; NR_134513; NR_134515; XM_017014704; XM_024447538; NM_001318156; XM_011517866; NR_134514; NR_164778; XM_011517867; XM_017014701; XM_024447539; NM_001376192; NM_001376193; XM_017014702; NM_001318157; NM_001376191; NR_164779; XM_017014700; NM_001170 LEP 3952 XM_005250340; NM_000230 GSTM5 2949 NM_000851; XM_005270785; XM_005270784 FMO2 2327 NM_001460; NR_160266; XR_921761; NM_001365900; XR_001737072; NM_001301347 PLIN1 5346 NM_002666; XM_005254934; NM_001145311 KIAA0101 9768 NR_109934; NM_001029989; NM_014736 CA3 761 NM_005181 CDO1 1036 NM_001323565; NR_136619; NM_001323567; NM_001801; NR_136618; NR_136620; NM_001323566; NR_136621 CSN1S1 1446 XM_006714091; NM_001025104; XM_006714089; XM_006714090; NM_001890 KIF4A 24137 NM_012310 GPD1 2819 NM_005276; NM_001257199 DPT 1805 NM_001937 ADH1B 125 NM_001286650; NM_000668 FABP4 2167 NM_001442 CENPF 1063 XM_017000086; NM_016343; XM_011509082 GABRD 2563 XM_011541194; XM_017000936; NM_000815 PFKFB1 5207 NM_001271804; XM_017029578; XM_017029576; NM_002625; NR_073450; XM_024452389; XM_017029577; NM_001271805 ATP1A2 477 NM_000702 CHL1 10752 XM_011533294; XM_017005568; XM_017005573; NM_001253387; NR_045572; XM_017005569; XM_017005572; XM_006712939; XM_011533292; XM_017005566; XM_006712940; XM_011533295; NM_001253388; NM_006614; XM_006712938; XM_011533296; XM_017005567; XM_017005570; XM_017005571 SLC7A10 56301 XM_011527120; XM_006723284; XM_024451609; XR_935841; NM_019849; XM_011527119; XM_024451610 ADIPOQ 9370 NM_004797; NM_001177800 EXO1 9156 XM_011544325; XM_011544322; NM_130398; XM_011544323; XM_006711840; NM_003686; NM_006027; XM_011544321; XM_011544324; XM_017002793; NM_001319224 INHBA 3624 XM_017012175; NM_002192; XM_017012176; XM_017012174 CES1 1066 NM_001025195; NM_001266; XM_005255774; NM_001025194 FOXM1 2305 XM_011520932; XM_011520934; NM_001243088; XM_011520930; XM_011520933; XM_011520935; XR_931507; NM_202003; NM_202002; XM_005253676; XM_011520931; NM_001243089; NM_021953 MMRN1 22915 XM_005262856; NM_001371403; NM_007351 HMMR 3161 NM_001142557; NM_001142556; NM_012484; NM_012485 PKMYT1 9088 NM_001258451; NM_182687; NM_001258450; XM_011522735; XM_024450490; NM_004203; XM_011522734; XM_011522736 CIDEA 1149 NM_001279; NR_134607; NM_001318383 CDC25C 995 XM_011543764; XM_011543760; XM_011543761; XM_011543763; NM_001364026; NM_001364027; XM_005272145; NM_001287582; NM_001287583; NM_001790; NM_022809; XM_006714739; XM_011543759; XM_011543762; NM_001318098; NM_001364028 OXTR 5021 NM_000916; NM_001354654; NM_001354655; NM_001354653; NM_001354656 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 IBSP 3381 NM_004967 PPP1R1A 5502 XM_005268995; XM_006719471; NM_006741 WISP1 8840 XM_024447319; NR_037944; XM_024447320; NM_080838; NM_003882; NM_001204870; XM_024447321; NM_001204869 STAB2 55576 NM_017564; XM_011538541; XM_011538538; XM_011538539; XM_011538542; XM_017019585; XM_011538537; XR_429107 CDKN3 1033 XM_024449458; NM_001330173; NM_005192; NM_001130851 TK1 7083 NM_001346663; NM_003258 KIF20A 10112 NM_005733 KCNB1 3745 XM_011528799; XM_006723784; NM_004975 S100B 6285 NM_006272; XM_017028424 PBK 55872 NM_018492; NM_001278945; NM_001363040 TDO2 6999 NM_005651 PITX1 5307 NM_002653 MCM10 55388 NM_182751; NM_018518; XM_011519538 GRM4 2914 NM_001256809; NM_001256812; NM_001256813; NM_001256811; NM_001256814; NM_001256810; NM_001282847; NM_000841 CST1 1469 NM_001898 AIM1L 55057 NM_017977; XM_011541672; XM_011541673; XR_001737260; NM_001039775; XR_946681; XM_005245918 TNMD 64102 NM_022144 CLEC5A 23601 XM_017011916; XM_017011915; XM_011515995; XM_017011917; NM_001301167; NM_013252 LRRC15 131578 NM_130830; NM_001135057 LAMP5 24141 NM_001199897; NM_012261 EPYC 1833 NM_004950; XM_011538008 RAB26 25837 XM_011522448; XM_011522450; NM_014353; NM_001308053 CST2 1470 NM_001322 NKAIN1 79570 NM_024522; XM_017002320 LALBA 3906 NM_002289; NM_001384350 CENPA 1058 NM_001809; NM_001042426 TUBB3 10381 NM_006086; NM_001197181 ARTN 9048 NM_057160; NM_057090; NM_001136215; NM_057091; NM_003976 TCL1B 9623 NM_004918; NM_199206 SYT13 57586 NM_001247987; NM_020826 CNTD2 79935 XM_006723395; NM_024877; XR_001753763; XR_935861 NEURL1 9148 XM_005270269; XM_011540333; XM_017016909; XM_011540332; XM_011540335; XR_945866; NM_004210; XM_005270270; XM_011540331 NPY2R 4887 NM_001370180; NM_000910; NM_001375470 CXCL10 3627 NM_001565; NR_168520 S100P 6286 NM_005980 MYT1 4661 NM_004535 ACTL8 81569 NM_030812; XM_011542212 HAPLN1 1404 XM_017009052; XM_017009051; NM_001884; XM_017009054; XM_017009053; XM_011543168 BTN1A1 696 NM_001732 CXCL9 4283 NM_002416 CEACAM6 4680 NM_002483; XM_011526990 FBN2 2201 NM_001999; XM_017009228 NAT1 9 NM_001160175; NM_001160170; NM_001160173; XM_011544688; XM_006716410; XM_017013947; NM_001160171; NM_001160172; NM_001160174; NM_001291962; XM_011544689; NM_001160176; XM_011544687; NM_000662; NM_001160179 FOXJ1 2302 NM_001454 BMPR1B 658 XM_017008558; NM_001203; NM_001256793; XM_011532201; NM_001256794; NM_001256792; XM_017008559; XM_017008560; XM_017008561 CNTNAP2 26047 XM_017011950; NM_014141 CEACAM5 1048 XM_011526322; XM_017026146; NM_001291484; NM_004363; XM_017026145; NM_001308398 KCNF1 3754 NM_002236 HOXC11 3227 NM_014212 KCNJ3 3760 NM_001260510; NM_001260508; NM_001260509; NM_002239 MAGEA12 4111 NM_001166386; NM_001166387; NM_005367 GABRQ 55879 NM_018558; XM_011531184 HHIPL2 79802 XM_024449814; XR_001737417; XR_426906; XM_017002350; XR_002957624; NM_024746; XR_001737416; XM_011509986 TLX1 3195 NM_001195517; XM_011539744; XM_011539745; NM_005521 SOX11 6664 NM_003108 MAGEA6 4105 NM_175868; NM_005363 CA9 768 XR_428428; NM_001216; XR_001746374 C2orf54 79919 XM_011511877; NM_001085437; NM_001282921; NM_024861 DIO1 1733 NM_000792; NM_001039715; NM_213593; NM_001039716; NM_001324316; NR_136692; NR_136693 F7 2155 XM_011537476; XM_011537475; NM_001267554; XM_011537474; NR_051961; XM_006719963; NM_019616; NM_000131 CYP2B6 1555 NM_000767 TRH 7200 NM_007117 CHGB 1114 NM_001819 PROL1 58503 NM_021225; NM_001302807; NR_126503 CD177 57126 XM_017027021; XM_017027022; NM_020406 KIF1A 547 NM_001379636; NM_001379637; NM_001379639; NM_001379650; NM_001330290; NM_001379633; NM_001379641; NM_001379651; NM_001379653; NM_004321; NM_001379632; NM_001379638; NM_001379645; NM_001379646; NM_001379649; NM_001379635; NM_001379640; NM_001379634; NM_001244008; NM_001379642; NM_001320705; NM_001330289; NM_001379631; NM_001379648 PSCA 8000 NR_033343; NM_005672 CRISP3 10321 NM_001368123; NM_006061; NM_001190986 PVALB 5816 NM_001315532; NM_002854 GAD1 2571 NM_013445; XM_017003758; NM_000817; XM_005246444; XM_011510922; XM_017003757; XM_017003756; XM_024452783 MYH7 4625 XM_017021340; NM_000257 SERPINB7 8710 XM_024451278; NM_001261831; NM_003784; NM_001040147; NM_001261830 COL2A1 1280 XM_017018831; XM_017018830; NM_001844; NM_033150; XM_017018828; XM_017018829 MSMB 4477 NM_138634; NM_002443 IRS4 8471 XM_006724713; NM_003604; NM_001379150; XM_011531061 BEX1 55859 NM_018476 PADI3 51702 NM_016233; XM_011541571; XM_017001463; XM_011541572 UGT2B4 7363 NM_001297616; NM_021139; NM_001297615 PRSS1 5644 NR_172951; XM_011516411; NR_172947; NM_002769; NR_172948; NR_172949; NR_172950 CYP2A7 1549 XR_935754; NM_000764; NM_030589 MSLN 10232 NM_001177355; NM_005823; NM_013404 CPB1 1360 NM_001871 CARTPT 9607 NM_004291 TGM4 7047 NM_003241; XM_011534042 NCAN 1463 NM_004386 CYP2A6 1548 NM_000762 CALML5 51806 NM_017422 TFF1 7031 NM_003225 Ovarian_Cancer QARS 5859 NR_073590; NM_005051; XM_017006965; NM_001272073 HSD17B2 3294 NM_002153; XR_001751898 CLDN6 9074 NM_021195 FEZF2 55079 NM_018008 SOX17 64321 NM_022454 HIF3A 64344 XM_017027133; XM_017027139; XM_024451649; XR_001753736; XR_935849; NM_022462; XM_017027132; XM_017027142; XM_005259152; XM_017027138; NM_152796; XM_005259156; XM_005259155; XM_017027136; XM_017027137; XR_002958343; XM_005259153; XM_017027135; XM_017027140; NM_152794; XM_017027134; XM_017027141; NM_152795 IZUMO4 113177 XM_024451343; XR_002958248; NM_001039846; XM_024451342; XM_024451344; NM_052878; NM_001031735; NM_001363588 PAQR4 124222 NM_001284513; NM_001284511; NM_001284512; NM_152341; NM_001324118 NGFR 4804 NM_002507 MCC 4163 NM_002387; NM_001085377 FAM107A 11170 NM_001076778; NM_007177; NM_001282713; NM_001282714 FOXL1 2300 NM_005250 KCNC3 3748 NM_004977; NR_110912; NM_001372305 PTGS2 5743 NM_000963 COL17A1 1308 NM_130778; NM_000494 FZD2 2535 NM_001466 EIF1AY 9086 NM_004681; NM_001278612 HOXD13 3239 XM_011511068; NM_000523; XM_011511069 FGF14 2259 NM_001321931; NM_001321943; NM_001321949; NM_175929; NM_001321933; NM_001321941; NM_001321932; NM_001321935; NM_001321937; NM_001321945; NM_001321947; NM_001321939; NM_001321936; NM_001321940; NM_001321944; NM_001321946; NM_001321948; NM_001379342; NM_001321934; NM_001321938; NM_001321942; NM_004115 SLC43A1 8501 XM_017018453; XM_024448727; XM_011545322; XM_011545321; XM_017018452; XM_011545320; XM_024448728; NM_001198810; XM_005274358; XM_017018451; NM_003627 MMP13 4322 NM_002427 LHX1 3975 NM_005568 CSDC2 27254 NM_014460 PAX9 5083 NM_001372076; NM_006194 B2M 567 XR_002957658; XM_005254549; NM_004048 SORBS2 8470 XM_005263312; XM_017008740; XM_017008751; XM_017008760; XM_017008764; XM_017008770; NM_001145674; NM_001270771; NM_001394266; NM_001395207; NM_021069; XM_017008738; XM_017008741; XM_017008748; XM_017008754; XM_017008762; XM_017008765; XM_017008766; NM_001145671; NM_001394247; NM_001394252; NM_001394258; NM_001394262; NM_001394263; NM_001394274; NM_001394275; NM_001394277; XM_017008743; XM_017008755; XM_017008758; XM_017008768; XM_017008771; XM_024454258; NM_001145672; NM_001394245; NM_001394246; NM_001394257; NM_001394260; NM_001394265; NM_001394267; XM_005263308; XM_005263310; XM_017008753; XM_017008763; XM_017008772; XM_017008774; XM_024454260; NM_001145675; NM_001394264; NM_001394272; XM_005263311; XM_005263313; XM_017008739; XM_017008756; XM_017008767; NM_001145670; NM_001145673; NM_001394256; NM_001394268; NM_001394270; NM_001394271; XM_005263307; XM_017008757; NM_001394248; NM_001394254; NM_001394261; NM_003603; XM_006714390; XM_017008750; XM_017008752; XM_017008769; XM_017008775; NM_001394249; NM_001394250; NM_001394255; NM_001394259; XM_006714388; XM_017008744; XM_017008759; XM_017008761; XM_017008773; XM_024454259; XM_024454257; XR_002959769; NM_001394251; NM_001394253; NM_001394273; NM_001394276 ZNF492 57615 NM_020855 ZBTB20 26137 NM_001164345; NR_121662; NM_001164347; NM_001348803; NM_001164343; NM_001393393; NM_001164342; NM_001348800; NM_001348801; NM_001348804; NM_001393395; NM_001393396; NM_001164344; NM_001348802; NM_001348805; NM_001393394; NM_001164346; NM_015642 PRSS1 5644 NR_172951; XM_011516411; NR_172947; NM_002769; NR_172948; NR_172949; NR_172950 PTGS1 5742 NM_001271166; XM_011518875; XM_024447615; NM_001271164; XM_005252105; XM_024447614; NM_000962; XM_011518876; NM_001271165; NM_001271367; NM_001271368; NM_080591 NOVA2 4858 XM_017026838; XM_006723230; NM_002516; XM_017026840; XM_017026839 IRX5 10265 NM_005853; XM_011522809; NM_001252197 DOK5 55816 XM_011528904; NM_001294161; NM_018431; XM_024451946; NM_177959 ASIP 434 NM_001385218; XM_011528820; NM_001672; XM_011528821 EMX2 2018 NM_004098; NM_001165924 RAPGEF3 10411 XM_011537758; XM_024448795; XR_001748551; XR_002957282; NM_001098532; XM_005268571; XM_017018688; NM_001098531; XM_011537752; XR_001748550; NM_006105; XM_011537755 VGLL1 51442 NM_016267 HSPA4L 22824 NM_001317381; NM_001317383; XM_011531745; NM_001317382; NM_014278 PAX8 7849 NM_013992; NM_013953; NM_013952; NM_003466; NM_013951 ALDH1A3 220 NM_001293815; NM_000693; NM_001037224 ANGPT4 51378 NM_001322809; XM_011529239; NM_015985 KIAA0513 9764 NM_001286565; NM_001297766; NM_001286566; XM_017023912; NM_014732; NM_001388359 RPS4Y1 6192 NM_001008 NES 10763 NM_024609; NM_006617 COL21A1 81578 XM_011514927; XM_024446561; XR_001743657; NM_030820; NR_134851; NR_134849; XM_011514925; NM_001318753; NR_134850; NM_001318752; NM_001318754; XM_011514926; XM_006715223; NM_001318751; XM_011514924 MNX1 3110 NM_001165255; NM_005515 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 SLC6A12 6539 XM_005253747; NM_003044; NM_001122847; XM_005253748; XM_011521010; XM_006719005; NM_001122848; NM_001206931 NPR1 4881 XM_017001374; XM_005245218; NM_000906 WISP3 8838 XM_011536223; XM_011536220; NM_198239; NR_125353; NR_125354; XR_001743705; NM_130396; XM_011536222; NM_003880 ASGR1 432 XM_011523861; NM_001197216; NM_001671 FOXL2 668 NM_023067 PNOC 5368 NM_006228; XM_011544559; XM_005273532; XM_017013578; NM_001284244 KLK6 5653 XM_024451611; NM_001319949; NM_001012964; NM_001319948; NM_001012965; NM_002774 ASGR2 433 XM_006721524; XM_011523866; XM_017024651; XM_024450755; NM_080913; XM_024450757; NM_001201352; XM_005256648; XM_011523865; NM_080912; XM_011523863; NM_080914; XM_006721526; XM_011523862; XM_011523864; XM_017024653; NM_001181; XM_017024652; XM_024450756 KLK10 5655 XM_006723289; XM_005259061; NM_002776; NM_145888; NM_001077500; XM_017026993; XM_006723287; XM_005259062 HEY1 23462 NM_001040708; NM_012258; NM_001282851 SCD 6319 NM_005063 DIO3 1735 NM_001362 SCGN 10590 NM_006998; XM_017010181 LGALS14 56891 NM_020129; NM_203471 SLC27A2 11001 NM_001159629; NM_003645 UTY 7404 XM_011531453; XM_011531464; XM_017030066; XM_017030067; NM_001258252; NM_001258260; NM_001258261; NM_001258270; NM_182659; NR_047597; NR_047618; NR_047621; XM_011531465; XM_024452493; NM_001258249; NM_001258251; NM_001258268; NR_047598; NR_047600; NR_047615; NR_047640; XM_006724875; XM_011531451; NM_001258269; NM_007125; NM_182660; NR_047606; NR_047616; NR_047620; NR_047631; NR_047639; NR_047641; NR_047647; XM_005262518; XM_011531454; XM_011531458; XM_011531459; XM_011531462; XM_017030073; XR_002958831; NM_001258257; NM_001258263; NM_001258266; NR_047601; NR_047611; NR_047613; NR_047619; NR_047627; NR_047634; NR_047645; NR_047646; XM_011531460; XM_011531461; XM_017030070; NM_001258256; NM_001258262; NM_001258264; NM_001258265; NR_047607; NR_047612; NR_047617; NR_047625; NR_047629; NR_047636; NR_047643; XM_011531442; XM_011531447; XM_011531450; XM_011531452; XM_017030074; XR_001756008; NM_001258253; NM_001258258; NM_001258259; NM_001258267; NR_047596; NR_047603; NR_047608; NR_047609; NR_047610; NR_047614; NR_047622; NR_047623; NR_047628; NR_047637; NR_047644; XM_011531448; XM_011531449; XM_017030068; XM_017030072; XM_024452494; NM_001258250; NR_047599; NR_047602; NR_047604; NR_047605; NR_047624; NR_047630; NR_047638; XM_011531441; XM_011531443; XM_011531445; XM_011531446; XM_011531455; XM_011531463; XM_017030071; NM_001258254; NM_001258255; NR_047626; NR_047635; NR_047632; NR_047633; NR_047642 BBC3 27113 XM_006723141; XM_011526722; NM_001127241; NM_001127242; NM_001127240; NM_014417 CETP 1071 XM_006721124; NM_000078; NM_001286085 GSTM5 2949 NM_000851; XM_005270785; XM_005270784 WNT7A 7476 XM_011534091; NM_004625 CCNE1 898 XM_011527440; NM_001238; NM_001322259; NM_001322261; NM_001322262; NM_057182 DLC1 10395 NM_001316668; NM_182643; XM_005273374; NM_001348081; NM_001348083; NM_001348084; NM_001164271; NM_006094; NM_024767; NM_001348082 RAMP3 10268 XM_017011666; NM_005856; XM_006715631 MEIS1 4211 NM_002398 SGCA 6442 XM_011525122; XM_011525120; XM_011525121; XM_024450873; NM_001135697; NR_135553; XR_002958056; XM_011525124; NM_000023; XM_011525123 HGH1 51236 NM_016458; XR_001745537 CHODL 140578 XM_017028273; NM_001204174; NM_024944; XM_011529453; NM_001204176; NM_001204175; NM_001204177; XM_011529457; NM_001204178 NLRP1 22861 NM_001033053; NM_033006; NM_033007; NM_014922; NM_033004 CLDN9 9080 NM_020982 RPL4 6124 NM_000968 CDH6 1004 NM_004932; NM_001362435; XM_017008910; XM_011513921; XR_001741972 TNFRSF10C 8794 NM_003841 ITGA2 3673 NR_073103; NR_073104; NR_073105; NR_073106; NR_073107; NM_002203 GRK5 2869 XM_005269707; XM_005269708; NM_005308 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 KDM5D 8284 XM_005262561; XR_002958832; XR_002958834; XR_002958837; XR_244571; NM_001146705; XM_011531468; XR_001756013; XM_024452495; XM_005262560; XM_024452496; XR_001756009; XR_001756011; XR_002958835; XR_001756010; NM_001146706; XR_002958836; XR_430568; NM_004653; XR_001756012; XR_002958833 TCF21 6943 NM_003206; NM_198392 SST 6750 NM_001048 IL20RA 53832 NM_001278722; XM_011535904; XM_017010955; NM_001278724; NM_014432; XM_006715506; NM_001278723; XM_017010954 FGF18 8817 NM_003862; NM_033649 NR5A1 2516 NM_004959 ULBP2 80328 NM_025217; XM_017011321 RNF128 79589 NM_024539; NM_194463 PRM2 5620 NM_001286358; NR_104428; NM_002762; NM_001286356; NM_001286359; NM_001286357 C7 730 NM_000587 L1CAM 3897 NM_024003; NM_001278116; NM_001143963; NM_000425 BCAM 4059 NM_001013257; NM_005581 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 ADRB3 155 NM_000025 CLDN16 10686 NM_006580; NM_001378492; NM_001378493 FMO5 2330 XM_005272946; XM_005272947; XM_011509351; XM_017000802; NM_001144829; NM_001461; XM_006711244; XM_006711245; XM_005272948; NM_001144830; XM_017000801; XM_011509350 KCNIP1 30820 NM_001034837; NM_014592; NM_001034838; NM_001278340; XM_017009407; XM_017009408; NM_001278339 FGF23 8074 NM_020638 PDE3B 5140 XR_001747903; NM_000922; NM_001363570; XM_017017912; XM_006718249; XM_017017911; NM_001363569 SLC4A3 6508 XM_011511667; NM_201574; NR_048551; XM_005246790; XM_011511665; NM_001326559; NM_005070 FOLR1 2348 NM_000802; NM_016729; NM_016730; NM_016725; NM_016724 STAR 6770 NM_001007243; NM_000349 Uterus_Carcinoma SPDEF 25803 NM_001252294; XM_005248988; NM_012391; XM_011514457 HLA-G 3135 XM_017010817; NM_001384280; XM_017010818; NM_002127; XM_024446420; NM_001363567; NM_001384290 MARCO 8685 NM_006770; XM_011512082; XM_011512083; XM_017005171 FEZF2 55079 NM_018008 SOX17 64321 NM_022454 HIF3A 64344 XM_017027133; XM_017027139; XM_024451649; XR_001753736; XR_935849; NM_022462; XM_017027132; XM_017027142; XM_005259152; XM_017027138; NM_152796; XM_005259156; XM_005259155; XM_017027136; XM_017027137; XR_002958343; XM_005259153; XM_017027135; XM_017027140; NM_152794; XM_017027134; XM_017027141; NM_152795 ZNF208 7757 NM_001329971; NM_001329973; NM_001329974; NM_001329972; NR_138252; NM_007153 CHRND 1144 NM_001311196; XM_011510524; NM_001256657; NM_001311195; NM_000751 SLC31A2 1318 NM_001860 C1S 716 XM_005253760; NM_001734; NM_001346850; NM_201442 GREB1 9687 XM_024453255; NM_014668; NM_033090; XM_024453254; XM_024453256; NM_148903; XM_005246196; XM_024453251; XR_922686; XM_024453250; XM_024453252; XM_011510418; XM_011510423; XM_011510422; XM_024453253; XM_011510419; XM_005246192; XR_001739081 VIP 7432 XM_006715562; XM_005267135; NM_003381; NM_194435 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 CREB5 9586 XM_017012807; XM_017012808; NM_001011666; XM_024447005; XM_017012806; XM_017012809; NM_182898; XM_017012810; XM_005249906; NM_004904; XR_001744893; XM_011515618; NM_182899 EIF1AY 9086 NM_004681; NM_001278612 E2F1 1869 NM_005225 NEBL 10529 XM_005252343; NM_001173484; NM_001377323; NM_001377327; XM_011519291; XR_001746996; XR_242691; NM_001377325; NM_001377324; NM_001377326; NM_213569; NM_001010896; NM_001377328; XM_005252344; NM_001377322; NM_001177483; XR_001746995; XM_005252342; XM_017015468; NM_006393; NM_016365 HOXD13 3239 XM_011511068; NM_000523; XM_011511069 CTSV 1515 NM_001201575; NM_001333 HOXD10 3236 NM_002148 DGKG 1608 NM_001346; NM_001080745; NM_001080744 SFRP1 6422 NM_003012 PAX9 5083 NM_001372076; NM_006194 SCGB2A1 4246 NM_002407 FOXJ1 2302 NM_001454 ZBTB20 26137 NM_001164345; NR_121662; NM_001164347; NM_001348803; NM_001164343; NM_001393393; NM_001164342; NM_001348800; NM_001348801; NM_001348804; NM_001393395; NM_001393396; NM_001164344; NM_001348802; NM_001348805; NM_001393394; NM_001164346; NM_015642 PTGS1 5742 NM_001271166; XM_011518875; XM_024447615; NM_001271164; XM_005252105; XM_024447614; NM_000962; XM_011518876; NM_001271165; NM_001271367; NM_001271368; NM_080591 NOVA2 4858 XM_017026838; XM_006723230; NM_002516; XM_017026840; XM_017026839 BEGAIN 57596 NM_001385092; NM_001385093; NR_169571; XM_024449671; NM_001385104; XM_024449670; NM_001159531; NM_001385088; NM_001385094; NM_001385095; NM_001385096; NM_001385097; NM_001385098; NM_001385099; NM_001385100; NM_020836; NM_001385089; NM_001385102; NM_001385083; NM_001385084; NM_001385091; NR_169570; NM_001385085; NM_001385086; NM_001385087; NM_001385103; NM_001385082; NM_001385090; NM_001385101 EMX2 2018 NM_004098; NM_001165924 VGLL1 51442 NM_016267 ALDH1A2 8854 NM_001206897; NM_170697; NM_170696; NM_003888 SLCO5A1 81796 XM_017013885; XR_928814; NM_001146008; NM_001146009; XM_017013886; XR_428341; XM_017013884; NM_030958; XM_017013883; XM_005251313 HOχA10 3206 NR_037939; NM_153715; NM_018951 GADD45G 10912 XM_011518163; NM_006705 RPS4Y1 6192 NM_001008 TPM2 7169 XM_017015091; NM_213674; XM_017015093; XM_017015088; NM_001301226; NM_001301227; NM_001145822; XM_017015087; XM_017015092; XM_017015090; NM_003289 MMP28 79148 XM_017025061; XM_017025062; NM_024302; XM_011525227; NM_001032278; NM_032950; XM_011525228; XM_011525225; XM_011525230; XM_024450943; XM_011525226; NR_111988; XM_011525229; XM_011525231; XM_011525232; XM_017025063; XM_017025064 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 MNX1 3110 NM_001165255; NM_005515 GAL3ST1 9514 XM_017029096; XM_024452304; NM_001318107; NM_001318111; NM_001318109; NM_001318114; XM_011530528; NM_001318105; NM_004861; XM_011530518; XM_011530524; NM_001318106; XM_011530522; XM_017029097; NM_001318108; NM_001318110; NM_001318103; NM_001318113; NM_001318116; XM_017029098; NM_001318104; NM_001318112; NM_001318115 ANKRD2 26287 NM_001291218; NM_001129981; NM_020349; NM_001291219; NM_001346793 EHHADH 1962 XM_006713525; NM_001166415; NM_001966 FXYD1 5348 NM_001278718; NM_001278717; NM_021902; XM_017026875; NM_005031; XM_017026874; XM_017026876 FOXL2 668 NM_023067 GLDC 2731 NM_000170 TNNC1 7134 NM_003280 EDNRB 1910 NM_001122659; NM_003991; NM_001201397; NM_000115; NR_047024 APOD 347 NM_001647 SLC27A2 11001 NM_001159629; NM_003645 SLC12A2 6558 XM_011543588; NM_001256461; XR_001742214; NR_046207; NM_001046; XM_017009771 FMO2 2327 NM_001460; NR_160266; XR_921761; NM_001365900; XR_001737072; NM_001301347 GSTM5 2949 NM_000851; XM_005270785; XM_005270784 SOX1 6656 NM_005986 APBA1 320 NM_001163; XM_011518617; XM_017014670; XM_005251968 HOXB13 10481 NM_006361 NPY4R 5540 XR_001747124; NM_001278794; NM_005972; XM_011539936; XM_017016387; XM_011539937; XM_017016386; XR_001747123 CIDEB 27141 NM_001393334; NM_001393340; NM_001318807; NM_001393339; NM_001393336; NM_001393338; NM_001393335; NM_001393337; NM_014430 MEIS1 4211 NM_002398 TNNC2 7125 NM_003279; XM_011529031 RIMBP2 23504 XM_017019105; XM_011538103; XM_011538105; NM_001351227; NM_001393620; NM_001393627; NM_001393616; NM_001351232; NM_001393615; NM_001393621; NM_001393623; NM_001393628; XM_011538106; XM_011538102; XM_011538108; NM_001351231; NM_001393614; NM_001393617; NM_001393622; NM_001393625; NM_001393629; NM_001351230; NM_001393619; NM_001393626; NM_001351228; NM_001393624; XM_011538107; XM_017019106; NM_001351226; NM_001351229; NM_001351233; NM_001393618; NM_015347 HGH1 51236 NM_016458; XR_001745537 SOX15 6665 NM_006942 PDLIM3 27295 NM_001114107; XR_938723; NM_001257963; XR_938724; NM_001257962; NR_047562; NM_014476; XR_001741206 CX3CR1 1524 NM_001171174; NM_001337; NM_001171171; NM_001171172 IL1RAP 3556 NM_001364880; NM_001167930; NM_001167931; NM_002182; NM_134470; NM_001167929; NM_001364879; NR_157353; NM_001167928; NM_001364881; NR_157352; XM_017006348 ZBTB16 7704 XR_001747955; NM_001354751; XM_017018259; NM_006006; NM_001354752; XM_005271658; XM_024448681; NM_001018011; NM_001354750 CLCA2 9635 NM_006536; XM_011542448 DLX5 1749 XM_017011803; NM_005221; XM_005250185 GABRQ 55879 NM_018558; XM_011531184 FOXA2 3170 NM_021784; NM_153675 TNFSF10 8743 NR_033994; NM_001190943; NM_003810; NM_001190942 IQCA1 79781 XM_017004960; NM_024726; NM_001270585; XM_011511865; XM_011511866; XM_011511864; NM_001270584; NR_073043 KDM5D 8284 XM_005262561; XR_002958832; XR_002958834; XR_002958837; XR_244571; NM_001146705; XM_011531468; XR_001756013; XM_024452495; XM_005262560; XM_024452496; XR_001756009; XR_001756011; XR_002958835; XR_001756010; NM_001146706; XR_002958836; XR_430568; NM_004653; XR_001756012; XR_002958833 TCF21 6943 NM_003206; NM_198392 TUBA1C 84790 NM_001303114; NM_032704; NM_001303116; NM_001303117; NM_001303115 GYPC 2995 NM_002101; XM_006712460; NM_001256584; NM_016815 CA2 760 NM_001293675; NM_000067 IL20RA 53832 NM_001278722; XM_011535904; XM_017010955; NM_001278724; NM_014432; XM_006715506; NM_001278723; XM_017010954 RGN 9104 XM_024452477; XM_006724568; XM_017029954; NM_004683; NM_001282848; NM_152869; NM_001282849; XM_006724567 AOC3 8639 XR_934584; NM_001277732; NM_003734; NR_102422; XM_011525419; XR_001752673; XM_011525420; XM_024451015; NM_001277731 FGF18 8817 NM_003862; NM_033649 MYO5A 4644 XM_011521607; NM_001142495; NM_001382348; XM_011521610; NM_000259; NM_001382347; XM_011521611; XM_011521609; XM_011521612; XM_017022227; NM_001382349 CCDC33 80125 XR_001751400; XM_011522090; XM_017022624; XM_017022626; NM_001287181; XM_011522088; XM_017022630; XR_001751401; NM_025055; XM_017022625; XM_017022628; XM_017022631; NR_108023; NM_182791; XM_011522087; XM_005254692; XM_017022627; XM_017022633; XM_017022623; XM_011522086; XM_017022632; XM_011522085; XM_011522089 REN 5972 NM_000537 NCAPG 64151 NM_022346; XM_017008543; NR_073124; XM_017008544; XM_011513876 CT62 196993 NR_168259; NM_001102658; NR_168260 CACNA1G 8913 NM_001256326; NM_001256328; NM_018896; NM_198378; NM_198388; NM_198396; NM_001256359; NM_001256361; NM_198383; NM_198385; NM_001256327; NM_001256330; NR_046056; NM_198380; NM_198382; NR_046054; XM_006722160; NM_198379; NM_001256329; NM_001256332; NM_001256333; NM_001256360; NM_198384; NM_198386; NR_046058; NM_001256325; NM_001256334; NM_198387; XM_006722161; NM_001256324; NM_001256331; NM_198376; NM_198377; NR_046055; NR_046057; NM_198397 PIGR 5284 XM_011509629; NM_002644 CSTA 1475 NM_005213 OSR2 116039 XM_017013018; NM_053001; XM_011516825; XM_005250778; NM_001286841; NM_001142462; XM_011516826; NM_001394683; XM_011516827 FOXF2 2295 NM_001452 TRO 7216 XM_011530814; XM_017029770; XM_024452433; NM_177557; XR_001755720; NM_001039705; NM_177556; NR_073149; XM_011530808; XR_001755721; XR_001755722; NM_001271183; NR_073148; XM_006724600; XM_011530809; XM_017029768; XM_017029771; XM_017029772; XM_017029773; XM_011530811; XM_011530812; NM_016157; XM_017029769; XM_011530813; XM_017029767; NM_001271184 GAD1 2571 NM_013445; XM_017003758; NM_000817; XM_005246444; XM_011510922; XM_017003757; XM_017003756; XM_024452783 NXPH4 11247 XM_017018747; NM_007224 DDX3Y 8653 NR_136716; NR_136718; NR_136719; NR_136721; NM_001122665; NR_136720; NR_136723; NM_004660; NM_001324195; XR_001756014; NM_001302552; NR_136717; NR_136724; NR_136722 EGFR 1956 NM_001346899; NM_201282; NM_201284; NM_001346898; NM_001346900; NM_001346897; NM_201283; NM_001346941; NM_005228 FMO3 2328 XM_011509345; XM_024454365; NM_001002294; NM_006894; NM_001319173; NM_001319174 TSPAN7 7102 NM_004615 ASRGL1 80150 XM_005274305; XM_005274306; XM_011545265; NM_001083926; XM_011545266; NM_025080; XR_002957199; XM_017018354; XR_002957198; XR_001747982 ALOX15B 247 NM_001141; NM_001039130; NM_001039131 PRPH 5630 XM_005269025; XR_944623; NM_006262; EFEMP1 2202 XM_024452757; NM_004105; NM_018894; XM_005264205; NM_001039349; XM_017003586; XM_024452755; XM_024452756; NM_001039348 SALL1 6299 NM_001127892; NM_002968 PRAME 23532 XM_011530034; NM_206954; NM_001318126; NM_001318127; NM_001291715; NM_001291719; NM_001291716; NM_006115; NM_001291717; NM_206953; NM_206956; NM_206955 PHOX2A 401 NM_005169 AQP5 362 NM_001651; XM_005268838 TTC22 55001 XM_017001582; XM_011541671; NM_001114108; NM_017904 Renal_Cell_Carcinoma SLC17A3 10786 NM_006632; NM_001098486 SLC4A1 6521 XM_011525129; XM_005257593; XM_011525130; NM_000342 CDH16 1014 NM_001204746; XM_011522807; NM_004062; XM_005255770; NM_001204744; NM_001204745 SLC22A2 6582 NM_153191; NM_003058 NAT8 9027 NM_003960 SLC3A1 6519 XM_011533047; NM_000341 ENPP3 5169 XR_001743464; NR_133007; NM_005021; XM_017010932; XM_011535897 FXYD2 486 NM_021603; NM_001127489; NM_001680 C14orf105 55195 XM_006720188; XR_001750402; NM_001283056; XM_006720189; XR_001750401; NM_001283057; NM_001283058; NM_001283059; XM_005267810; NM_018168; XM_005267813; XM_005267806; XM_005267811; XR_001750400; XM_005267814; NM_001283060 SIM1 6492 XM_011536072; NM_001374769; NM_005068 GALNT14 79623 NM_001253827; XR_001738942; XR_001738941; NM_001329095; XM_017004907; NM_001253826; XR_001738943; XM_017004906; NM_001329097; NM_001329096; NM_024572 PAX2 5076 NM_001304569; NM_003987; NM_001374303; NM_003989; NM_000278; NM_003990; NM_003988 PVALB 5816 NM_001315532; NM_002854 RHBG 57127 XR_001737323; NR_146765; XR_001737328; XR_001737329; NR_046115; XM_011509799; XM_017001859; NR_146764; XM_011509800; XM_017001858; XR_001737324; XR_001737325; NM_001256395; NR_146763; XM_017001857; NM_020407; XR_001737330; XR_001737332; NM_001256396 AQP2 359 NM_000486 POU3F3 5455 NM_006236 PAX8 7849 NM_013992; NM_013953; NM_013952; NM_003466; NM_013951 GFRA3 2676 NM_001496 CA12 771 NM_001218; NR_135511; NM_206925; NM_001293642 FOXD3 27022 NM_012183 CACNG4 27092 NM_014405 HAND2 9464 NM_021973 NLGN1 22871 NM_001365923; NM_001365928; NM_001365932; NM_014932; XM_011512551; XM_011512553; XM_017005897; XM_017005902; NM_001365929; NM_001365926; XM_017005895; XM_017005893; NM_001365925; NM_001365931; XM_017005896; XM_017005900; NM_001365933; XM_005247237; NM_001365930; NM_001365936; XM_011512554; XM_017005888; XM_017005894; NM_001365924; NM_001365927; NM_001365934; NM_001365935 TRPM3 80036 NM_001366147; XM_011519045; NM_001366145; NM_206944; XM_011519042; XM_024447681; NM_001007470; NM_001366152; NM_001366153; NM_206946; XM_011519037; NM_001366151; NM_206947; XM_011519040; NM_001007471; NM_001366141; NM_001366150; NM_001366154; XM_011519039; XM_017015156; XM_024447687; NM_001366144; NM_001366146; NM_020952; XM_024447683; NM_001366149; XM_011519038; XM_011519046; XM_024447682; XM_024447684; XM_024447685; XM_024447686; NM_001366142; NM_001366143; NM_001366148; NM_024971; NM_206945; NM_206948 ARHGEF4 50649 XM_011511276; XM_005263689; XR_001738756; NM_001375900; NM_001375902; XM_011511274; XR_001738757; NM_001375901; NM_001375904; NM_001367493; NM_001375903; NM_015320; NM_001395416; NM_032995; XM_005263688; XM_011511277; XM_017004231; XM_024452938 INSM1 3642 NM_002196 S100A14 57402 XM_017001875; NM_020672; XM_005245362 LGR5 8549 NR_110596; NM_001277227; NM_001277226; NM_003667 CFTR 1080 NM_000492 TRHDE 29953 XM_017019244; XM_017019243; NM_013381; XM_005268819; XM_011538248 ESRP1 54845 XM_005250991; NM_001122827; NM_017697; XM_005250992; NM_001122826; NM_001034915; NM_001122825 LAD1 3898 NM_005558 GRHL2 79977 XM_011517306; XM_024447286; NM_001330593; NM_024915; XM_011517307 ALPPL2 251 NM_031313 HOXC10 3226 NM_017409 EPHB3 2049 NM_004443 SLC6A11 6538 NM_001317406; XM_017007073; XM_011534033; NM_014229 NKX3-2 579 NM_001189 CNKSR1 10256 NM_006314; NR_023345; NM_001297647; NM_001297648 RAMP1 10267 XM_017003153; XM_017003154; XM_017003155; NM_001308353; NM_005855; XM_017003152; XM_017003156 KIF2C 11004 NM_001297656; XM_011540541; NM_001297657; XM_011540540; NM_006845; NM_001297655 ST8SIA2 8128 NM_006011; NM_001330416; XM_017022642 SFRP1 6422 NM_003012 SPAG4 6676 XM_011529009; NM_003116; XM_005260520; NM_001317931 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 SIGLEC8 27181 XM_011526734; NM_014442; NM_001363548 SLC14A2 8170 XM_017026016; NM_007163; NM_001242692; XM_024451271; NM_001371319; XM_024451270 PLA2G7 7941 NM_001168357; XR_001743639; XM_005249408; NM_005084; XR_002956305 KCNN1 3780 NM_001386974; NM_001386976; NR_170373; NM_001386975; NM_001386977; NM_002248; XM_011528004; NR_170374 CA8 767 NM_001321837; NM_001321838; XM_011517587; XM_011517588; NM_001321839; NM_004056; NR_135821; XM_017013818 KLK6 5653 XM_024451611; NM_001319949; NM_001012964; NM_001319948; NM_001012965; NM_002774 CA9 768 XR_428428; NM_001216; XR_001746374 Squamous_Cell_Carcinoma TMPRSS11D 9407 XM_005265710; XM_017008851; NM_004262 SPRR1B 6699 NM_003125 SERPINB3 6317 NM_006919 DSG3 1830 XM_011525850; NM_001944 ADH7 131 NM_001166504; NM_000673 S100A12 6283 NM_005621 SPRR1A 6698 NM_005987; NM_001199828 KRT1 3848 NM_006121 SERPINB13 5275 NM_001348267; XM_011526029; NM_001348268; NM_012397; NM_001348269; NM_001307923; NM_001348270 KRT6A 3853 NM_005554 CRNN 49860 NM_016190 FOXE1 2304 NM_004473 SFTPB 6439 XM_005264487; NM_198843; XM_005264488; NM_000542; NM_001367281; XM_005264490 CALML3 810 NM_005185 CRCT1 54544 NM_019060; XM_011509656 SFN 2810 NM_006142 TP63 8626 NM_001114978; NM_001329144; NM_001329146; NM_001329964; NM_001329145; NM_003722; NM_001114979; NM_001114982; NM_001329149; NM_001114980; NM_001114981; NM_001329150; NM_001329148 SFTPA2 729238 XM_011540124; XM_005270132; NM_001320813; NM_001320814; XM_017016608; XM_011540125; NM_001098668; XM_005270128 FABP5 2171 NM_001444 KRT5 3852 NM_000424 GPR87 53836 NM_023915 CKM 1158 NM_001824 MYL2 4633 NM_000432 SOX2 6657 NM_003106 MYL1 4632 NM_079422; NM_079420 IRX4 50805 NM_016358; NM_001278633; NM_001278632; NM_001278635; NM_001278634 NKX2-1 7080 NM_001079668; NM_003317 KRT20 54474 NM_019010 NR1H4 9971 NR_135146; XM_006719719; NM_001206978; NM_001206993; NM_001206977; XM_011539040; XM_011539042; NM_001206979; NM_005123; XM_011539041; NM_001206992 PLA2G3 50487 XM_011530205; XR_937865; XM_011530204; NM_015715 FLG 2312 NM_002016 SFTPD 6441 XM_011540087; NM_003019; XM_011540088 TNNT3 7140 NM_001042781; NM_001363561; NM_001367847; NM_001367849; XM_006718299; XM_017018207; XM_017018208; XM_017018217; XM_024448669; XM_024448670; XM_024448671; XM_011520343; XM_017018211; XM_017018215; NM_001297646; NM_001367848; NM_001367850; XM_006718294; XM_006718300; XM_017018212; XM_017018219; NM_001042780; NM_001367845; XM_006718288; XM_017018209; XM_017018210; XM_017018218; NM_001367852; XM_017018206; XM_017018213; XM_024448672; NM_001367843; NM_001367844; NM_001367846; NM_001367851; XM_017018214; XM_017018216; NM_001042782; NM_001367842; XM_017018205; NM_006757 SPINK1 6690 NM_003122; NM_001379610; NM_001354966 NTS 4922 NM_006183 MMP12 4321 NM_002426 ALDH3B2 222 NM_001354345; NM_001393400; NM_001393402; ; NM_001393401; NM_000695; NM_001031615 HNF1B 6928 XM_011525161; NM_001165923; NM_001304286; XM_011525163; NM_000458; XM_011525162; NM_006481; XM_011525164; XM_011525160 UPK1B 7348 NM_006952 GJB1 2705 NM_000166; XM_011530907; NM_001097642 FABP4 2167 NM_001442 CTSV 1515 NM_001201575; NM_001333 HOXD11 3237 NM_021192 CLDN18 51208 NM_001002026; NM_016369 PITX1 5307 NM_002653 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 FZD10 11211 NM_007197 CYP4B1 1580 XM_011540833; NR_135003; XM_011540832; NM_000779; NM_001319161; NM_001319163; NM_001099772; XM_017000466; NM_001319162; XR_946559 TCN1 6947 NM_001062 CLDN3 1365 NM_001306 MYOT 9499 XM_017010060; XM_017010061; NM_001300911; NM_001135940; XM_017010062; NM_006790 LAMC2 3918 NM_005562; NM_018891; XM_017001273 SCNN1B 6338 XM_017023526; XM_011545913; XM_011545914; XM_017023525; NM_000336 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 CSF3 1440 NR_168489; NR_168491; NM_000759; NM_172220; NM_001178147; NM_172219; NR_168490; NR_033662 HMGCS2 3158 NM_001166107; XM_011541313; NM_005518 AQP4 361 NM_001317387; NM_001364287; NM_001364286; NM_001317384; XM_011525942; NM_001650; NM_001364289; NM_004028 TMC5 79838 NM_001261841; NM_024780; NM_001308161; NM_001105248; NM_001105249 SLC52A1 55065 XM_011523951; NM_001104577; NM_017986 DMBT1 1755 XM_011539390; XM_011539391; XM_011539407; XM_011539408; NM_007329; XM_006717660; XM_006717665; XM_011539402; XM_024447854; XM_011539392; XM_011539393; XM_011539400; XM_011539403; XM_011539405; XM_011539413; XM_017015798; NM_001320644; NM_004406; XM_011539394; XM_011539409; XM_011539415; NM_017579; XM_011539389; XM_011539395; XM_011539396; XM_011539399; XM_011539401; XM_011539410; XM_011539414; NM_001377530; XM_011539398; XM_011539411 SLC34A2 10568 NM_001177999; NM_006424; NM_001177998 GABRQ 55879 NM_018558; XM_011531184 PRSS3 5646 NM_007343; NM_001197097; NM_002771; XM_011517965; NM_001197098 SLC4A4 8671 XM_024454267; XM_024454271; XM_024454272; NM_001098484; XM_024454270; NM_003759; XM_017008793; XM_024454268; NM_001134742; XM_024454269; XM_011532390; XM_017008792 COX6A2 1339 NM_005205 SERPINA5 5104 NM_000624 SDC1 6382 NM_001006946; XM_005262620; XM_005262621; NM_002997; XM_005262622 ENDOU 8909 NM_001172439; NM_006025; NM_001172440 UPK1A 11045 NM_007000; NM_001281443 NME5 8382 XM_024446227; NM_003551; XM_005272099; XM_024446228; XM_017009945 SORBS2 8470 XM_005263312; XM_017008740; XM_017008751; XM_017008760; XM_017008764; XM_017008770; NM_001145674; NM_001270771; NM_001394266; NM_001395207; NM_021069; XM_017008738; XM_017008741; XM_017008748; XM_017008754; XM_017008762; XM_017008765; XM_017008766; NM_001145671; NM_001394247; NM_001394252; NM_001394258; NM_001394262; NM_001394263; NM_001394274; NM_001394275; NM_001394277; XM_017008743; XM_017008755; XM_017008758; XM_017008768; XM_017008771; XM_024454258; NM_001145672; NM_001394245; NM_001394246; NM_001394257; NM_001394260; NM_001394265; NM_001394267; XM_005263308; XM_005263310; XM_017008753; XM_017008763; XM_017008772; XM_017008774; XM_024454260; NM_001145675; NM_001394264; NM_001394272; XM_005263311; XM_005263313; XM_017008739; XM_017008756; XM_017008767; NM_001145670; NM_001145673; NM_001394256; NM_001394268; NM_001394270; NM_001394271; XM_005263307; XM_017008757; NM_001394248; NM_001394254; NM_001394261; NM_003603; XM_006714390; XM_017008750; XM_017008752; XM_017008769; XM_017008775; NM_001394249; NM_001394250; NM_001394255; NM_001394259; XM_006714388; XM_017008744; XM_017008759; XM_017008761; XM_017008773; XM_024454259; XM_024454257; XR_002959769; NM_001394251; NM_001394253; NM_001394273; NM_001394276 HAND1 9421 NM_004821; XM_005268531 CRH 1392 NM_000756 TFAP2A 7020 NM_001032280; XM_006715175; NM_001042425; XM_017011232; XM_011514833; NM_001372066; NM_003220 COL9A1 1297 NM_001851; NR_165185; NM_078485; XM_017010246; XM_011535429; XM_017010247; NM_001377289; NM_001377290; NM_001377291 ATP10B 23120 XM_011534472; XM_017009253; NM_001366652; NM_001366658; XM_011534468; NM_001366653; NM_001366654; NM_001366655; NM_001366656; NM_025153; NM_001366657; XM_017009252; XM_011534469 ALDOB 229 NM_000035 AHNAK2 113146 NM_138420; XM_024449463; NM_001350929 BCAS1 8537 XM_005260591; XM_017028111; XM_005260595; NM_001366295; XM_005260590; XM_011529090; NM_001366298; XM_005260594; XM_005260589; XM_011529091; NM_001366297; NM_001316361; NM_003657; NM_001323347; NM_001366296 EVX1 2128 NM_001304519; NM_001304520; NM_001989 CLDN4 1364 NM_001305 NEB 4703 XM_005246590; XM_005246594; XM_005246602; XM_005246611; XM_017004178; XM_017004179; XM_017004180; NM_001164508; XM_005246603; XM_005246617; XM_006712542; XM_017004185; NM_001164507; NM_001271208; XM_005246593; XM_005246598; XM_005246606; XM_005246610; XM_017004177; XM_017004184; NM_004543; XM_005246592; XM_005246599; XM_005246601; XM_005246616; XM_017004181; XM_005246604; XM_005246608; XM_017004182; XM_017004183; XM_005246591; XM_005246596; XM_005246597; XM_006712541; XM_011511225; XM_011511226; XM_005246613; XM_005246612; XM_005246615; XM_011511227 LRP2 4036 XM_011511183; NM_004525; XM_011511184 DLX2 1746 NM_004405 GRIK3 2899 NM_000831 TBX1 6899 NM_005992; NM_080646; XM_017028928; XM_006724312; XM_017028926; NM_001379200; XM_017028925; XM_017028927; NM_080647 XDH 7498 NM_000379; XM_011533096; XM_011533095 DLX6 1750 NM_005222 ADH1C 126 NM_000669; NR_133005 HKDC1 80201 NM_025130; XR_001747209; XM_011540195 MFAP5 8076 NM_001297709; NR_123733; NR_123734; NM_001297711; NM_003480; NM_001297710; NM_001297712 DNAJC22 79962 NM_001304944; NM_024902; XM_005269157; XM_005269155; XM_005269156 HNF4G 3174 NM_001330561; XM_017013373; XM_017013375; XM_017013374; XM_017013376; NM_004133 KCNB1 3745 XM_011528799; XM_006723784; NM_004975 ACTG2 72 NM_001199893; NM_001615 SSX1 6756 NM_001278691; NM_005635 NELL2 4753 XM_017019343; XM_017019344; NM_001145107; XM_011538396; NM_001145109; XM_017019341; NM_001145110; XM_017019342; NM_006159; XM_005268905; NM_001145108 AGER 177 XR_001743190; NM_001206940; XM_017010328; NM_001206936; NM_001206954; NM_172197; XR_001743189; NM_001136; NM_001206929; NM_001206932; NM_001206934; NR_038190; NM_001206966 FAM107A 11170 NM_001076778; NM_007177; NM_001282713; NM_001282714 SEMA3G 56920 XM_024453642; NM_020163 FIGF 2277 NM_004469 TCF21 6943 NM_003206; NM_198392 FMO2 2327 NM_001460; NR_160266; XR_921761; NM_001365900; XR_001737072; NM_001301347 CHRM2 1129 NM_000739; NM_001006631; NM_001006632; NM_001378972; NM_001006630; NM_001006633; NM_001006628; NM_001006626; NM_001006627; NM_001378973; NM_001006629 AOC3 8639 XR_934584; NM_001277732; NM_003734; NR_102422; XM_011525419; XR_001752673; XM_011525420; XM_024451015; NM_001277731 ADH1B 125 NM_001286650; NM_000668 GDF10 2662 NM_004962 MYOC 4653 NM_000261 SOX17 64321 NM_022454 FHL5 9457 NM_001170807; NM_001322466; NM_001322467; NM_020482 PDK4 5166 NM_002612 CCL23 6368 NM_005064; XR_429910; NM_145898 MMP11 4320 NM_005940; NR_133013 HBB 3043 NM_000518 HOXA10 3206 NR_037939; NM_153715; NM_018951 MYBL2 4605 NM_002466; NM_001278610 UBE2C 11065 NM_001281742; NM_001281741; NM_181802; NM_181803; NR_104036; NR_104037; NM_007019; NM_181800; NM_181801; NM_181799 NPY1R 4886 NM_000909; XM_005263031; XM_011532010 TUBB3 10381 NM_006086; NM_001197181 ORC6 23594 NR_037620; NM_014321; XM_011522978 GRIN2D 2906 XM_011526872; NM_000836 PRR4 11272 NM_001098538; NM_007244 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 FOLR1 2348 NM_000802; NM_016729; NM_016730; NM_016725; NM_016724 ONECUT2 9480 NM_004852 MMP9 4318 NM_004994 HOXA11 3207 NM_005523 HOXB13 10481 NM_006361 CST1 1469 NM_001898 SYT12 91683 XM_011545346; XM_011545347; NM_177963; XM_017018547; NM_001177880; NM_001318775; XM_017018548; XM_006718737; XM_024448766; NM_001318773 STRA6 64220 NM_022369; NM_001199042; XM_011521883; XM_011521885; NM_001142618; XM_017022479; NM_001142617; NM_001142619; NM_001142620; XM_011521884; XR_931877; XM_017022478; XM_017022480; NM_001199040; NM_001199041 NXPH4 11247 XM_017018747; NM_007224 CXCL13 10563 NM_001371558; NM_006419 CDX2 1045 XM_011534876; NM_001354700; XM_011534879; XM_011534875; XM_011534878; NM_001265 COL11A1 1301 XM_017000337; XM_017000335; XM_017000336; NR_134980; NM_080629; XM_017000334; NM_001190709; NM_001854; NM_080630 RAB3B 5865 XM_017001958; NM_002867 JPH3 57338 NM_001271604; NR_073379; NM_001271605; NM_020655 Lung_Adenocarcinoma SFTPA2 729238 XM_011540124; XM_005270132; NM_001320813; NM_001320814; XM_017016608; XM_011540125; NM_001098668; XM_005270128 BPIFA1 51297 NM_130852; NM_001243193; NM_016583 LGSN 51557 XM_017010931; XM_017010929; XM_011535889; XM_011535892; NM_016571; XM_017010930; NM_001143940 SCGB1A1 7356 NM_003357 NKX2-1 7080 NM_001079668; NM_003317 SFTPC 6440 NM_001317779; NM_001385656; NM_001385658; NM_001385659; NM_001172410; NM_001385654; NM_001385655; NM_001317778; NM_001317780; NM_001385657; NM_001385660; NM_001385653; XM_011544613; NM_001172357; NM_003018 SFTPB 6439 XM_005264487; NM_198843; XM_005264488; NM_000542; NM_001367281; XM_005264490 C4BPA 722 XM_005273252; NM_000715; XM_005273251 CEACAM6 4680 NM_002483; XM_011526990 AGER 177 XR_001743190; NM_001206940; XM_017010328; NM_001206936; NM_001206954; NM_172197; XR_001743189; NM_001136; NM_001206929; NM_001206932; NM_001206934; NR_038190; NM_001206966 SERPINB13 5275 NM_001348267; XM_011526029; NM_001348268; NM_012397; NM_001348269; NM_001307923; NM_001348270 SPRR1A 6698 NM_005987; NM_001199828 HAND2 9464 NM_021973 TMC5 79838 NM_001261841; NM_024780; NM_001308161; NM_001105248; NM_001105249 TSPAN8 7103 NM_001369760; NM_004616; XM_006719583 SPDEF 25803 NM_001252294; XM_005248988; NM_012391; XM_011514457 SCEL 8796 XM_006719884; XM_011535281; XM_011535284; XM_011535285; XM_011535288; XM_011535289; NM_144777; XM_006719882; XM_011535291; XM_017020805; XM_006719885; XM_011535283; XM_011535287; XM_011535290; NM_003843; XM_005266578; NM_001160706; XM_011535282; XM_011535286 CP 1356 XM_006713500; XM_006713501; XM_017005735; XM_017005734; XM_006713499; XM_011512435; XR_427361; NM_000096; NR_046371 GCNT3 9245 NM_004751 CLDN8 9073 NM_199328; NM_012132 CARTPT 9607 NM_004291 FOXA1 3169 NM_004496; XM_017021246 EDN3 1908 NM_207034; XM_024451847; NM_207032; XR_002958461; XR_002958462; XR_936513; NM_001302455; NM_207033; XM_006723734; XM_011528655; XM_024451848; NM_000114; XM_005260312; XM_005260313; NM_001302456 CCL13 6357 NM_005408 DNAH2 146754 XM_017024219; XM_024450606; XM_024450608; XM_024450609; XM_011523663; XM_024450604; XM_024450605; XM_024450607; NM_001303270; NM_020877; XM_011523667; XM_024450610; XM_011523670 EMX2 2018 NM_004098; NM_001165924 CDHR1 92211 XM_011540338; NM_033100; NM_001171971; XM_011540340; XM_011540337; XM_011540339 RNF186 54546 NM_019062 TBX4 9496 XM_011525490; XM_011525491; NM_001321120; XM_011525495; NM_018488 LAMB3 3914 XM_005273124; NM_001127641; XM_017001272; NM_000228; NM_001017402 S100A7 6278 NM_002963 PLA2G2A 5320 NM_001161728; NM_000300; NM_001161729; NM_001161727; NM_001395463 KCNG1 3755 XM_011528800; XM_011528802; XM_011528803; XM_011528805; NM_172318; NM_002237; XM_011528801; XM_011528804; XM_011528806; XM_006723785 KRT5 3852 NM_000424 BARX1 56033 NM_021570 SLC44A4 80736 NM_001178045; NM_001178044; NM_025257 MPPED2 744 NM_001377952; NM_001145399; NR_165347; XM_005253111; NR_165336; NR_165343; NR_165339; NR_165340; NR_165345; XM_024448676; NM_001377954; XM_005253114; NM_001377953; NR_165337; NR_165344; NR_165348; XM_017018231; NR_165346; NM_001377955; NM_001377956; NM_001584; NR_165338; NR_165341; NR_165342 XDH 7498 NM_000379; XM_011533096; XM_011533095 CCL25 6370 NM_001394634; NM_001394635; NM_001394638; NM_005624; NM_148888; NM_001394636; NM_001201359; NM_001394637 S100A1 6271 NM_006271 ACTA1 58 NM_001100 HR 55806 XM_006716367; NM_005144; XM_005273569; NM_018411 DLL3 10683 NM_016941; NM_203486 KRT13 3860 NM_153490; NM_002274 CBLC 23624 XM_011526690; XM_011526688; XR_935783; XM_005258696; XR_243917; XM_011526689; NM_001130852; NM_012116 FAM107A 11170 NM_001076778; NM_007177; NM_001282713; NM_001282714 TCF21 6943 NM_003206; NM_198392 FCN3 8547 NM_173452; NM_003665 FABP4 2167 NM_001442 GRIA1 2890 NM_001114183; NM_001258022; NM_001258023; NM_001364166; XM_017009392; NR_157093; NM_000827; NM_001258019; NM_001258020; NM_001364165; NM_001258021; NR_047578; NM_001364167 ALAS2 212 NM_001037968; NM_001037967; NM_000032; NM_001037969 TFAP2A 7020 NM_001032280; XM_006715175; NM_001042425; XM_017011232; XM_011514833; NM_001372066; NM_003220 PITX1 5307 NM_002653 IGF2BP3 10643 XM_011515092; NM_006547; XM_011515089; XM_006715639; XM_011515090; XM_011515091; XM_011515093 RASAL1 8437 XR_002957386; NM_001193521; NM_001394081; NM_001394082; XM_005253950; NM_001394084; NM_001394087; NM_004658; XM_017020030; XM_017020031; XM_006719642; XR_001748903; XM_006719641; NM_001301202; NM_001394083; XM_011538854; XM_017020029; NM_001394089; XR_001748902; NM_001193520; NM_001394085; NM_001394086; NM_001394088 MMP11 4320 NM_005940; NR_133013 PTPRH 5794 XM_011527188; XM_017027061; NM_001161440; XM_017027058; XR_001753731; XM_017027056; XM_017027062; XM_017027059; XM_011527183; XR_001753730; XM_017027063; XM_017027064; XM_011527190; XM_017027057; XM_017027060; NM_002842 NXPH4 11247 XM_017018747; NM_007224 CXCL14 9547 NM_004887 Prostate_Adenocarcinoma RNF128 79589 NM_024539; NM_194463 PRM2 5620 NM_001286358; NR_104428; NM_002762; NM_001286356; NM_001286359; NM_001286357 CENPF 1063 XM_017000086; NM_016343; XM_011509082 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 NKX3-1 4824 NM_001256339; NR_046072; NM_006167 CGREF1 10669 NM_001166239; NM_006569; NM_001301324; NM_001166241; NM_001166240 KLK2 3817 NM_005551; NR_045762; NM_001002231; NM_001002232; NM_001256080; NR_045763 SEMG1 6406 NM_198139; NM_003007 ASPN 54829 NM_001193335; NM_017680 DEPDC1 55635 NM_001114120; NM_017779 AMACR 23600 NM_203382; NM_001167597; NM_001167598; NM_014324; NM_001167596; NM_001167595 COL6A1 1291 NM_001848 ONECUT2 9480 NM_004852 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 TRPM8 79054 XM_017004891; NM_024080; XM_011511810; XM_024453132; XM_024453134; XM_024453133 ATP8A2 51761 XM_011535103; XM_011535113; XM_005266419; XM_024449369; XM_011535109; NM_016529; XM_011535104; XM_017020626; NM_001313741; XM_017020625; XM_011535106; XM_011535107 PGC 5225 NM_002630; NM_001166424 GDPD3 79153 NM_024307 MKI67 4288 NM_002417; NM_001145966; XM_006717864; XM_011539818 ZIC1 7545 NM_003412 ADAMTSL4 54507 XM_011509650; XR_001737242; XM_011509648; NM_001378596; XM_011509645; XM_011509652; NM_001288607; XM_011509651; NM_019032; XM_011509649; XM_017001506; XM_011509644; XM_017001507; NM_001288608; XR_921844; NM_025008 APOC1 341 NM_001645; NM_001321066; NM_001379687; NM_001321065 PLP2 5355 NM_002668 HOXB13 10481 NM_006361 DLX2 1746 NM_004405 TDRD1 56165 XM_024448081; NM_001385365; NM_001365891; NM_001385366; NM_001385372; NM_001395205; XM_011539959; XM_017016415; NM_001385363; NM_001385368; XM_011539960; NM_001385364; XM_011539964; XM_011539962; XM_011539961; NM_001385367; NM_001385369; NM_001385371; NM_198795; NM_031278; XM_017016414; NM_001385370 SCN1A 6323 NM_001353960; NM_001202435; NM_001353951; NM_001353952; NM_001353958; NM_001353950; NM_001353957; NR_148667; NM_001353949; NM_001353954; XR_001738884; NM_001353955; NM_001353961; NM_001165963; NM_001165964; NM_001353948; NM_006920; XR_001738883 TRPC4 7223 NM_001354806; XM_011535206; NM_016179; NM_003306; NM_001135958; NM_001135957; NM_001372055; XM_017020723; NM_001135956; NM_001354799; NM_001135955 TRO 7216 XM_011530814; XM_017029770; XM_024452433; NM_177557; XR_001755720; NM_001039705; NM_177556; NR_073149; XM_011530808; XR_001755721; XR_001755722; NM_001271183; NR_073148; XM_006724600; XM_011530809; XM_017029768; XM_017029771; XM_017029772; XM_017029773; XM_011530811; XM_011530812; NM_016157; XM_017029769; XM_011530813; XM_017029767; NM_001271184 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 KIF4A 24137 NM_012310 CCNJL 79616 NM_001308173; NM_024565; NR_131769; XM_011534646; XM_017009847; XM_006714917; XR_427810; XM_011534647; XM_017009848; XR_427811 PAGE4 9506 NM_001318877; NM_007003 TSPYL2 64061 XM_006724592; XM_017029727; NM_022117; XR_001755719; XM_017029726 MMP9 4318 NM_004994 HOXD13 3239 XM_011511068; NM_000523; XM_011511069 TPX2 22974 XM_011528697; XM_011528699; NM_012112; XM_011528700 FHL5 9457 NM_001170807; NM_001322466; NM_001322467; NM_020482 GRIA3 2892 NM_007325; NM_181894; NM_000828; NM_001256743 IFI6 2537 NM_002038; XM_024446207; NM_022873; NM_022872 RPL4 6124 NM_000968 ISL1 3670 XM_011543380; NM_002202 HPN 3249 NM_002151; NM_182983; XM_017026732; NM_001384133; XM_017026731; NM_001375441 SRD5A2 6716 XM_011533069; NM_000348; XM_011533072 ACPP 55 NM_001099; XM_011512946; NM_001134194; XM_011512947; NM_001292037 GUCY2C 2984 NM_004963; XM_011520631 HOXC6 3223 NM_153693; NM_004503 LILRB4 11006 NM_001278429; NM_001394939; XM_017026215; NM_001394934; NM_006847; NM_001278428; XM_017026216; NM_001394935; NM_001081438; NM_001394938; XR_002958246; NM_001278426; NM_001394933; NM_001394937; NM_001278427; NM_001278430; NM_001394936 MSMB 4477 NM_138634; NM_002443 STAR 6770 NM_001007243; NM_000349 KLK3 354 NM_001030050; NM_001030047; NM_145864; NM_001030049; NM_001030048; NM_001648 FOXF1 2294 NM_001451 Urinary_Bladder_Urothelial_Carcinoma UPK2 7379 NM_006760 PLA2G2F 64600 NM_022819; NM_001360869; XM_011541955; XM_011541956 CYP1A1 1543 NM_001319216; NM_001319217; NM_000499 S100A2 6273 NM_001366407; NM_001366406; NM_005978 IVL 3713 NM_005547 VGLL1 51442 NM_016267 UPK3A 7380 NM_006953; NM_001167574 DHRS2 10202 NM_005794; XM_006720001; XM_005267249; NM_001318835; XR_001750107; XM_011536338; XR_001750106; XR_943366; NM_182908; XR_001750105; XR_943367; XM_011536339 SERPINB4 6318 NM_175041; NM_002974; XM_011526138 UPK1B 7348 NM_006952 KRT20 54474 NM_019010 TMEM40 55287 NM_001284408; NM_018306; XM_011533937; NM_001284406; NM_001284407 BHMT 635 NM_001713 GATA3 2625 XM_005252443; NM_002051; XM_005252442; NM_001002295 KRT6A 3853 NM_005554 MSMB 4477 NM_138634; NM_002443 SLC14A1 6563 XM_005258333; XM_024451238; XR_001753266; NM_001146037; XM_005258329; NM_001146036; NM_001308278; NM_015865; XM_011526144; NM_001308279; XM_006722526; XM_011526142; NM_001128588 SFTPA2 729238 XM_011540124; XM_005270132; NM_001320813; NM_001320814; XM_017016608; XM_011540125; NM_001098668; XM_005270128 PPARG 5468 NM_001354669; NM_001354670; NM_001374263; NM_001330615; NM_001374262; NM_005037; NM_001374261; NM_138711; NM_138712; NM_001374264; NM_001374266; NM_001354668; NM_015869; NM_001354667; NM_001354666; NM_001374265 TNNT3 7140 NM_001042781; NM_001363561; NM_001367847; NM_001367849; XM_006718299; XM_017018207; XM_017018208; XM_017018217; XM_024448669; XM_024448670; XM_024448671; XM_011520343; XM_017018211; XM_017018215; NM_001297646; NM_001367848; NM_001367850; XM_006718294; XM_006718300; XM_017018212; XM_017018219; NM_001042780; NM_001367845; XM_006718288; XM_017018209; XM_017018210; XM_017018218; NM_001367852; XM_017018206; XM_017018213; XM_024448672; NM_001367843; NM_001367844; NM_001367846; NM_001367851; XM_017018214; XM_017018216; NM_001042782; NM_001367842; XM_017018205; NM_006757 OLFM4 10562 NM_006418 ACTC1 70 NM_005159 GJB1 2705 NM_000166; XM_011530907; NM_001097642 AIM1L 55057 NM_017977; XM_011541672; XM_011541673; XR_001737260; NM_001039775; XR_946681; XM_005245918 IL9R 3581 XM_011545650; XM_017029496; XM_017029499; XM_017030050; XM_017030051; XM_011531155; XM_017029498; XM_017029502; XM_017029505; XM_017030053; XM_017030055; NM_176786; XM_011531156; XM_011545645; XM_011545651; XM_017029495; XM_017029501; XM_017030054; XM_011531152; XM_011545649; XM_017030045; XM_017030046; XM_017030052; XM_017029497; XM_017030049; XM_011531157; XM_011531154; XM_017029500; XM_017029503; XM_017030044; XM_017030047; NM_002186; XM_011531151; XM_011545646; XM_011545652; XM_017029504; XM_017029506; XM_017030048 NRAP 4892 XM_005269867; NM_006175; NM_001322945; NM_198060; XM_005269865; XM_011539832; XM_024448029; NM_001261463; XM_006717870; XM_005269864 SLC5A1 6523 NM_000343; XM_011530331; NM_001256314 SFTPC 6440 NM_001317779; NM_001385656; NM_001385658; NM_001385659; NM_001172410; NM_001385654; NM_001385655; NM_001317778; NM_001317780; NM_001385657; NM_001385660; NM_001385653; XM_011544613; NM_001172357; NM_003018 CASQ1 844 NM_001231 ACTG2 72 NM_001199893; NM_001615 POU3F3 5455 NM_006236 UNC93A 54346 XM_011535908; NM_001143947; XM_011535905; XM_011535907; NM_018974; XM_017010958; XM_011535906 TRPA1 8989 XM_011517624; NM_007332; XM_011517625; XM_017013946 KCNIP1 30820 NM_001034837; NM_014592; NM_001034838; NM_001278340; XM_017009407; XM_017009408; NM_001278339 DPP6 1804 NM_001364499; NR_157196; NM_001364500; XM_017011812; NM_001290252; NM_001364498; NM_001364501; NM_001039350; NM_001936; NM_130797; NR_157195; NM_001290253; NM_001364502; NM_001364497 MSLN 10232 NM_001177355; NM_005823; NM_013404 COX6A2 1339 NM_005205 CCL11 6356 NM_002986 IRX4 50805 NM_016358; NM_001278633; NM_001278632; NM_001278635; NM_001278634 REG1A 5967 NM_002909 MAGEA11 4110 XM_017029522; NM_001011544; NM_005366; XM_011531164 GAL3ST1 9514 XM_017029096; XM_024452304; NM_001318107; NM_001318111; NM_001318109; NM_001318114; XM_011530528; NM_001318105; NM_004861; XM_011530518; XM_011530524; NM_001318106; XM_011530522; XM_017029097; NM_001318108; NM_001318110; NM_001318103; NM_001318113; NM_001318116; XM_017029098; NM_001318104; NM_001318112; NM_001318115 HLF 3131 NM_002126; XM_011524705; XR_002957996; NM_001330375; XM_005257269 HAND1 9421 NM_004821; XM_005268531 HPN 3249 NM_002151; NM_182983; XM_017026732; NM_001384133; XM_017026731; NM_001375441 SLC34A2 10568 NM_001177999; NM_006424; NM_001177998 TFF3 7033 NM_003226 PNMAL1 55228 NM_001103149; NM_018215; XM_011527067 PITX2 5308 NM_001204397; NM_153427; XM_024454090; NM_000325; NM_001204398; NM_001204399; NM_153426 REG3A 5068 NM_138938; NM_002580; NM_138937 CHRM2 1129 NM_000739; NM_001006631; NM_001006632; NM_001378972; NM_001006630; NM_001006633; NM_001006628; NM_001006626; NM_001006627; NM_001378973; NM_001006629 PENK 5179 NM_006211; NM_001135690 CDHR2 54825 NM_001171976; NM_017675 MMP11 4320 NM_005940; NR_133013 CDH4 1002 NM_001252339; NM_001794; NM_001252338 FOXA2 3170 NM_021784; NM_153675 HOXB8 3218 NM_024016; XM_005257286; XM_017024564 GABRA3 2556 NM_000808; XM_006724811 SLC47A1 55244 NM_018242 F7 2155 XM_011537476; XM_011537475; NM_001267554; XM_011537474; NR_051961; XM_006719963; NM_019616; NM_000131 S100A1 6271 NM_006271 DNAJC22 79962 NM_001304944; NM_024902; XM_005269157; XM_005269155; XM_005269156 NPR3 4883 NM_001363652; NM_001364460; NM_000908; XM_011514047; XM_011514049; XM_017009492; NM_001204375; NM_001364458; NM_024563; XM_011514050; NM_001204376 FOXE1 2304 NM_004473 ALS2CL 259173 XR_427263; XR_940409; XR_940410; NR_033815; XR_001740091; XR_001740094; XR_001740095; XM_011533572; XR_001740090; XR_940406; XR_940407; XR_940408; XR_940412; NM_182774; NM_182775; NR_135622; XR_001740092; XR_001740097; XR_002959507; NM_001190707; XM_005265025; XM_006713093; XR_001740093; NM_147129; XM_006713094; XM_006713091; XR_001740096; XR_940405 ACADL 33 NM_001608; XM_005246517; XM_017003955 ARSE 415 XM_017029526; NM_001369079; NM_001369080; XM_005274521; XM_011545521; NM_000047; XM_005274519; NM_001282628; NM_001282631 AQP5 362 NM_001651; XM_005268838 HOXA11 3207 NM_005523 CYP2W1 54905 NM_017781; XM_011515440; XM_011515441 KBTBD11 9920 XM_017014115; XM_011534772; XM_017014117; XM_017014114; XM_017014116; XM_011534771; NM_014867 TCF21 6943 NM_003206; NM_198392 ADAMTSL3 57188 NM_207517; XM_024450000; XR_931873; XM_017022435; XM_011521822; XM_011521823; XM_017022434; NM_001301110; XM_011521825; XM_011521824 GLP2R 9340 XM_011524077; NM_004246; XM_017025340; XM_005256861; XM_017025339; XM_017025341 CFD 1675 NM_001317335; NM_001928 FAM107A 11170 NM_001076778; NM_007177; NM_001282713; NM_001282714 TPPP 11076 XM_024454346; XM_005248237; XM_017008993; NM_007030 FOXF1 2294 NM_001451 HSPB6 126393 NM_144617 P2RX1 5023 XM_006721529; XM_011523898; XR_934029; NM_002558; XM_011523896; XM_011523897; XM_011523899; XM_011523900; XR_934030 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 SGCD 6444 NM_000337; NM_172244; XM_005265967; XM_011534621; XM_017009723; XM_005265966; XM_017009724; NM_001128209 ESM1 11082 NM_001135604; NM_007036 DPT 1805 NM_001937 GFRA1 2674 XM_011539634; NM_001348098; NM_001382557; NM_005264; NM_001382558; NM_001348099; NM_001382560; NM_001382559; NM_001145453; NM_001348096; NM_145793; NM_001382556; NM_001382561 SPP1 6696 NM_001251829; NM_001040060; NM_001251830; NM_000582; NM_001040058 CMA1 1215 NM_001836; NM_001308083 FBN2 2201 NM_001999; XM_017009228 MSI1 4440 XM_011538362; XM_011538361; XM_011538366; XM_011538365; XM_011538370; NM_002442; XM_011538364; XM_011538371; XM_006719403; XM_006719404; XM_011538363; XM_011538368 TERT 7015 NR_149162; NM_198255; NM_198253; NR_149163; NM_001193376; NM_198254 VGF 7425 NM_003378; XM_011516549; XM_005250561 CEDN9 9080 NM_020982 FOER1 2348 NM_000802; NM_016729; NM_016730; NM_016725; NM_016724 Melanoma PAX3 5077 NM_181457; NM_000438; NM_181459; NM_181460; NM_001127366; NM_013942; NM_181461; NM_181458 IRF4 3662 NM_001195286; NR_046000; NR_036585; XM_006715090; NM_002460 TYR 7299 XM_011542970; NM_000372 GAPDHS 26330 NM_014364 PMEL 6490 NM_001200054; NM_001200053; NM_001320121; NM_001384361; NM_001320122; NM_006928 TYRP1 7306 NM_000550; XR_001746372 ALX1 8092 XM_011538782; NM_006982 MLANA 2315 NM_005511 CDH19 28513 XM_011525931.3; XM_017025711.2; XM_011525932.1 SOX10 6663 NM_006941 MIA 8190 NM_006533; NM_001202553 PLP1 5354 NM_001128834; NM_000533; NM_001305004; NM_199478 SLC6A15 55117 XM_011538525; NM_018057; NM_001146335; NM_182767 PRAME 23532 XM_011530034; NM_206954; NM_001318126; NM_001318127; NM_001291715; NM_001291719; NM_001291716; NM_006115; NM_001291717; NM_206953; NM_206956; NM_206955 KRT2 3849 NM_000423 MFSD12 126321 XM_017026288; XM_011527684; NM_021731; NM_174983; NM_001287529; XM_005259490; NM_001042680; XM_006722647 APOD 347 NM_001647 KCNK1 3775 NM_002245; XM_011544184 EFHD1 80303 NM_001243252; NM_001308395; NM_025202 CRCT1 54544 NM_019060; XM_011509656 KRT8 390601, 149501, 3856 NM_001256293; NM_002273 GPM6B 2824 NM_001001996; XM_017029432; NM_001318729; NM_005278; NM_001001995; XM_005274489; XM_011545497; NM_001001994 CNTNAP2 26047 XM_017011950; NM_014141 STEAP1B 256227 NM_207342; NM_001382447; NM_001164460 RGN 9104 XM_024452477; XM_006724568; XM_017029954; NM_004683; NM_001282848; NM_152869; NM_001282849; XM_006724567 FA2H 79152 XM_011523319; XM_011523317; NM_024306 TRPV2 51393 XM_011523922; XM_017024730; XM_011523925; XM_017024732; XM_005256677; XM_017024731; XM_006721541; XM_005256678; XM_011523923; NM_016113; XM_005256676; XM_006721543 CLDN7 1366 NM_001307; NM_001185022; NM_001185023 SPINT2 10653 NM_001166103; NM_021102 CD24 100133941 NM_001291739; NR_117090; NR_117089; NM_001291738; NM_001291737; NM_013230; XM_024446293; NM_001359084 HNF1B 6928 XM_011525161; NM_001165923; NM_001304286; XM_011525163; NM_000458; XM_011525162; NM_006481; XM_011525164; XM_011525160 SUSD4 55061 XM_011509687; XM_017001584; XM_017001586; XM_017001587; XM_024447937; XM_024447940; XM_005273169; XM_017001588; XM_017001585; XM_024447936; NM_017982; XM_005273172; XM_006711408; XM_011509685; XM_017001583; XM_017001589; NM_001037175 ST8SIA3 51046 NM_015879 GABRE 2564 XM_024452360; NM_021990; NM_021984; XM_011531140; XM_017029388; XM_017029389; NM_004961; NM_021987; XM_017029387 PHACTR1 221692 XM_017010452; XM_017010454; XM_017010458; XM_017010465; NM_001322311; NM_001374582; NM_001374583; NM_001374584; NM_001322309; XM_005248934; XM_017010460; NM_001322308; NM_001374581; XM_017010459; XM_017010464; NM_001242648; NM_001322314; XM_017010462; NM_001322312; XM_017010456; XM_017010457; XM_017010466; NM_030948; XM_017010455; NM_001322310; XM_017010469; NM_001322313 ASS1 445 XM_017014729; XM_005272200; XM_011518705; NM_000050; NM_054012 CDS1 1040 XM_017007649; NM_001263; XM_017007650; XM_017007651; XM_005262687; XM_017007648 PLEKHG6 55200 NM_018173; XM_017019555; NM_001384602; NM_001384603; XM_006718985; NM_001384604; NR_169277; XM_011520967; NM_001144857; NM_001384599; NR_169278; NM_001144856; NM_001384598; NM_001384600; NM_001384601 CACNG4 27092 NM_014405 CYTL1 54360 NM_018659; XM_017008299 PITX1 5307 NM_002653 HOXD13 3239 XM_011511068; NM_000523; XM_011511069 CNIH3 149111 NR_136288; NR_136294; NR_136297; NM_152495; NR_136292; NM_001322305; NM_001322303; NR_136293; NR_136296; NR_136295; NR_136287; NM_001322304; NR_136290; NR_136291; NM_001322302; NR_136289 CFB 629 NM_001710 MYH7 4625 XM_017021340; NM_000257 CLU 1191 NM_001831; NR_045494; NR_038335 SCG5 6447 NM_001144757; NM_001394278; NM_001394279; NM_003020 SH3GL3 6457 XR_001751374; NM_001324184; NM_001324186; XM_017022486; XR_931878; XR_001751372; NR_136712; XR_931880; XR_931882; NM_001301109; NM_001324185; NR_125370; NR_136714; XM_011521892; XR_001751375; XR_931879; NM_001301108; NM_001324183; NM_003027; NR_136713; XM_011521889; XM_011521891; XM_024450017; XR_001751373; XR_002957669; NM_001324182; NM_001324187; NR_136711 RBM47 54502 XM_005248108; XM_017008307; XM_024454098; NM_001371113; XM_005248103; XM_017008306; XM_017008309; XM_017008310; NM_001098634; NM_019027; XM_011513707; XM_005248109; XM_017008304; XM_017008308; NM_001371114; XM_011513708 FUT6 2528 XM_011527875; NM_000150; NM_001381956; NM_001369504; NM_001381957; NM_001381958; NM_001369502; NM_001381959; NM_001369505; NM_001381955; XM_011527872; NM_001040701 FGFR2 2263 XM_017015924; NM_001144919; XM_006717708; XM_017015925; NM_001144915; NM_001144917; NM_022975; NM_023028; XM_024447890; NM_000141; NM_001144913; NM_001320654; NM_022970; NR_073009; NM_022971; NM_022973; NM_023030; XM_006717710; XM_024447887; XM_024447888; NM_001320658; NM_022976; XM_017015920; NM_001144918; NM_022974; NM_023031; XM_024447889; XM_024447891; XM_024447892; NM_023029; XM_017015921; NM_001144914; NM_001144916; NM_022972 DLX2 1746 NM_004405 LAD1 3898 NM_005558 SPP1 6696 NM_001251829; NM_001040060; NM_001251830; NM_000582; NM_001040058 ADH1B 125 NM_001286650; NM_000668 MYL2 4633 NM_000432 ZBTB16 7704 XR_001747955; NM_001354751; XM_017018259; NM_006006; NM_001354752; XM_005271658; XM_024448681; NM_001018011; NM_001354750 CKM 1158 NM_001824 FCGR1A 2209 NM_001378804; NM_001378805; NM_001378807; NM_001378810; NR_166122; NR_166123; NM_001378809; NM_001378811; NM_001378808; NR_166121; NM_000566; NM_001378806 CCL5 6352 NM_001278736; NM_002985 DBNDD1 79007 NM_001288709; NM_001288708; NM_001371581; NM_001042610; NM_024043 SDS 10993 NM_006843 CXCR3 2833 XM_017029435; XM_017029436; NM_001504; NM_001142797; XM_005262256; XM_005262257 MMP27 64066 XM_011542950; XM_017018120; XM_011542948; NM_022122; XM_011542949 TREM2 54209 NM_001271821; NM_018965 CCR5 1234 NM_001100168; NM_001394783; NM_000579 C1QA 712 NM_015991; NM_001347465; NM_001347466 B4GALNT1 2583 XM_024448928; XR_002957307; XM_011538147; XM_024448929; NM_001276469; XM_017019141; NM_001276468; XM_005268773; XM_017019140; NM_001478; XM_017019142 ONECUT2 9480 NM_004852 FAM155B 27112 XM_011530908; XM_011530909; NM_015686 DKK1 22943 NM_012242 LOR 4014 NM_000427; XM_024447049 Liver_Neoplasm APCS 325 NM_001639 ITIH2 3698 NM_002216 CRP 1401 NM_000567; NM_001329058; NM_001382703; NM_001329057 CPB2 1361 XM_017020393; NM_016413; NM_001872; NM_001278541 ITIH1 3697 NM_001166436; NM_002215; NM_001166434; NM_001166435 ASGR2 433 XM_006721524; XM_011523866; XM_017024651; XM_024450755; NM_080913; XM_024450757; NM_001201352; XM_005256648; XM_011523865; NM_080912; XM_011523863; NM_080914; XM_006721526; XM_011523862; XM_011523864; XM_017024653; NM_001181; XM_017024652; XM_024450756 APOC3 345 NM_000040 GC 2638 XM_006714177; NM_001204306; NM_001204307; NM_000583 CYP2C8 1558 NM_001198854; NM_001198855; NM_030878; NM_000770; NM_001198853 C8G 733 NM_000606; XR_245338 APOA2 336 NM_001643 ALB 213 NM_000477 ART4 420 NM_021071; NM_001354646 AGT 183 NM_000029; NM_001384479; NM_001382817 PROZ 8858 NM_003891; XR_001749709; XR_001749708; XM_017020812; XR_001749707; NM_001256134; XM_017020813 GRIK3 2899 NM_000831 CRABP1 1381 NM_004378 DRD2 1813 XM_017017296; NM_016574; NM_000795 CYP21A2 1589 NM_000500; NM_001128590; XM_024452555; NM_001368143; NM_001368144 DBH 1621 NM_000787 L1CAM 3897 NM_024003; NM_001278116; NM_001143963; NM_000425 KLK8 11202 NM_007196; NM_144505; NR_104008; NM_144507; NM_144506; NM_001281431 EPS8L3 79574 XM_017002329; XM_011542135; XM_011542134; NM_139053; NM_001319952; NM_024526; XM_011542133; XM_017002328; XR_001737407; XM_017002327; NM_133181; XM_011542132; XR_001737406 SFRP5 6425 NM_003015 GATA4 2626 NM_001308093; NM_002052; NM_001308094; NM_001374273; NM_001374274 MAB21L2 10586 NM_006439 GRIK5 2901 XM_011526870; XM_011526868; XM_011526865; XM_011526867; XM_011526869; XM_011526862; XM_011526871; XM_017026713; NM_002088; XR_935810; NM_001301030 HOXA7 3204 NM_006896 GLB1L2 89944 NM_001370460; NM_001370463; NM_001370461; NM_001370462; NM_138342 PCSK2 5126 NM_002594; NM_001201529; NM_001201528 TERT 7015 NR_149162; NM_198255; NM_198253; NR_149163; NM_001193376; NM_198254 PLP1 5354 NM_001128834; NM_000533; NM_001305004; NM_199478 CXCL14 9547 NM_004887 KRT4 3851 NM_002272 SFTPC 6440 NM_001317779; NM_001385656; NM_001385658; NM_001385659; NM_001172410; NM_001385654; NM_001385655; NM_001317778; NM_001317780; NM_001385657; NM_001385660; NM_001385653; XM_011544613; NM_001172357; NM_003018 SLC5A1 6523 NM_000343; XM_011530331; NM_001256314 GPRC5A 9052 NM_003979 GPM6B 2824 NM_001001996; XM_017029432; NM_001318729; NM_005278; NM_001001995; XM_005274489; XM_011545497; NM_001001994 NNAT 4826 NM_001322802; NM_181689; NM_005386 BDH1 622 XM_005269355; XM_017007012; XM_017007013; NM_004051; XM_017007015; XM_017007007; XM_011513067; XM_017007008; XM_017007009; XR_001740229; NM_203314; XM_017007010; NM_203315; XM_005269352; XM_017007011 ADAMTS13 11093 NM_139027; NM_139028; XM_017014235; NM_139026; XM_017014233; XR_001746171; NM_139025; XM_017014232; XM_011518176; XM_017014234; XM_011518178; XM_011518179; NR_024514 COLEC10 10584 XM_005250756; NM_006438; NM_001324095 GABRD 2563 XM_011541194; XM_017000936; NM_000815 GDF2 2658 NM_016204 COL15A1 1306 XM_011518214; NM_001855 S100A12 6283 NM_005621 MDK 4192 NM_001012334; XM_011520116; XM_017017764; NM_001270550; NM_001270551; NM_001012333; NM_001270552; NM_002391; NR_073039 PTTG1 9232 XM_024446260; NM_001282382; NM_001282383; NM_004219 ESM1 11082 NM_001135604; NM_007036 DEPDC1 55635 NM_001114120; NM_017779 THBS4 7060 XR_002956176; XM_017009798; NM_001306214; NM_003248; NM_001306213; XM_017009799; NM_001306212 HOXD9 3235 NM_014213 OLFML2B 25903 NM_001297713; XM_017000967; NM_001347700; NM_015441; XM_011509398 MMP11 4320 NM_005940; NR_133013 PRSS1 5644 NR_172951; XM_011516411; NR_172947; NM_002769; NR_172948; NR_172949; NR_172950 C1QTNF3 114899 NR_146599; NM_181435; NM_030945 Thyroid_Neoplasm TG 7038 XM_006716622; XM_017013800; XM_017013793; XM_017013795; XM_017013798; XM_017013796; XM_017013797; XM_017013794; XM_005251038; XM_005251040; NM_003235; XM_017013799; XM_005251042 DCSTAMP 81501 XM_024447289; NM_030788; XM_024447290; NM_001257317; XM_011517324; XM_024447288; XM_011517321 DAPK2 23604 XM_017022049; XM_017022051; NM_001384998; NM_001395289; NM_001395290; NM_001395293; XM_011521413; NM_001384999; NM_001395284; NM_014326; XM_017022043; NM_001395288; NM_001395291; NR_169522; NR_172521; XM_017022046; NM_001384997; NM_001385000; NM_001395286; NM_001395287; XM_011521421; XM_017022044; XM_017022047; XM_017022052; NM_001395285; NM_001395292; XM_017022048; XM_017022050; NM_001395282; NR_169524; XM_011521414; XM_011521415; XM_017022045; NM_001395279; NM_001395283; NR_169523; NM_001363730; NM_001395281 SLC26A4 5172 XM_017012318; XM_005250425; NM_000441; XM_006716025 TPO 7173 XM_024453088; XM_024453087; NM_175722; XM_024453091; XM_024453085; XM_024453086; NM_001206745; XM_024453090; NM_175719; NM_175721; NM_175720; XM_024453093; XM_011510380; NM_001206744; XM_024453089; XM_024453092; NM_000547 TSHR 7253 XM_011537119; XM_005268039; XM_005268037; NM_000369; NM_001142626; XM_006720245; NM_001018036 KCNJ16 3773 XM_006721885; NM_170742; NM_001291625; NM_018658; XM_017024609; NM_001291622; NM_001291623; XM_017024610; NM_001270422; NM_170741; XM_005257337; XM_006721887; XM_011524781; NM_001291624; XM_006721886 NKX2-1 7080 NM_001079668; NM_003317 FOXE1 2304 NM_004473 CLDN16 10686 NM_006580; NM_001378492; NM_001378493 GABRB2 2561 NM_000813; NM_021911; NM_001371727 MATN1 4146 NM_002379 INPP5J 27124 NM_001284289; XM_017028772; NM_001284288; NM_001284285; NM_014422; NM_001284286; NM_001284287; XM_011530143; NM_001002837 TOX3 27324 NM_001080430; XM_017023142; NM_001146188; XM_005255892; XM_011523002; XM_024450230 TRPC5 7224 XM_017029774; NM_012471 HHEX 3087 NM_002729 PAX8 7849 NM_013992; NM_013953; NM_013952; NM_003466; NM_013951 FOXD3 27022 NM_012183 COL4A3 1285 XM_017003295; XM_005246280; XM_006712245; XM_005246277; XR_241280; XM_011510556; NM_000091; NM_031363; NM_031364; NM_031365; XM_011510555; XR_001738601; NM_031362; NM_031366 S100A5 6276 XM_017002031; NM_001394233; NM_001394234; XM_017002032; NM_001394232; NM_002962; XM_017002029 GFRA3 2676 NM_001496 NELL1 4745 NM_001288713; NM_006157; NM_201551; NM_001288714 DUSP9 1852 XM_011531123; NM_001395; NM_001318503; XM_011531124 AZGP1 563 NM_001185 BMP8A 353500 XM_017001198; XM_006710616; XM_011541381; XM_011541382; XR_946642; XR_946640; XR_946641; NM_181809 LECT1 11061 XM_011534898; XM_011534899; NM_001011705; NM_007015; XM_011534900; XM_011534897 DIO2 1734 NM_001366496; NM_000793; NM_001324462; NR_158991; NM_001242503; NM_013989; NR_158990; NM_001007023 LRRC2 79442 XM_011534110; XM_017007177; XR_001740264; NM_024750; NM_024512 HOXA7 3204 NM_006896 HOXA10 3206 NR_037939; NM_153715; NM_018951 SLC5A5 6528 XM_011528194; XM_011528193; NM_000453; XM_017027158; XM_011528192 AADAC 13 NM_001086; XM_005247104 KCNJ15 3772 XM_017028344; XM_017028343; XM_011529561; NM_170736; NM_170737; XM_005260975; NM_001276438; NM_001276439; NM_002243; XM_006724002; XM_011529560; XM_017028345; NM_001276435; NM_001276436; NM_001276437 CACNA1I 8911 NM_021096; XM_017029035; XM_017029036; XM_017029037; NM_001003406 GPC3 2719 NM_004484; XM_017029413; NM_001164618; NM_001164617; NM_001164619 KLHDC8A 55220 NM_001271863; NM_001271865; XM_024448121; NM_018203; NM_001271864 SSX1 6756 NM_001278691; NM_005635 SYT12 91683 XM_011545346; XM_011545347; NM_177963; XM_017018547; NM_001177880; NM_001318775; XM_017018548; XM_006718737; XM_024448766; NM_001318773 BMPR1B 658 XM_017008558; NM_001203; NM_001256793; XM_011532201; NM_001256794; NM_001256792; XM_017008559; XM_017008560; XM_017008561 MYL2 4633 NM_000432 CLIC3 9022 XM_017015282; NM_004669; XM_017015281 SPINK1 6690 NM_003122; NM_001379610; NM_001354966 S100A1 6271 NM_006271 BIRC5 332 NM_001168; NM_001012271; NM_001012270 UBE2C 11065 NM_001281742; NM_001281741; NM_181802; NM_181803; NR_104036; NR_104037; NM_007019; NM_181800; NM_181801; NM_181799 AMN 81693 XM_024449714; XM_011537203; NM_030943; XM_011537202 CBLN1 869 NM_004352 PBK 55872 NM_018492; NM_001278945; NM_001363040 ALK 238 NM_004304; NM_001353765; XM_024452779; XR_001738688; XM_024452778 CYP2J2 1573 NR_134982; NR_134981; NM_000775 TSPAN8 7103 NM_001369760; NM_004616; XM_006719583 CHGA 1113 NM_001301690; NM_001275; XM_011536370 FOXM1 2305 XM_011520932; XM_011520934; NM_001243088; XM_011520930; XM_011520933; XM_011520935; XR_931507; NM_202003; NM_202002; XM_005253676; XM_011520931; NM_001243089; NM_021953 SCD 6319 NM_005063 SCN4A 6329 NM_000334 TF 7018 NM_001063; NM_001354703; NM_001354704 TPX2 22974 XM_011528697; XM_011528699; NM_012112; XM_011528700 TFAP2A 7020 NM_001032280; XM_006715175; NM_001042425; XM_017011232; XM_011514833; NM_001372066; NM_003220 ACADL 33 NM_001608; XM_005246517; XM_017003955 IQCA1 79781 XM_017004960; NM_024726; NM_001270585; XM_011511865; XM_011511866; XM_011511864; NM_001270584; NR_073043 CENPF 1063 XM_017000086; NM_016343; XM_011509082 EYA1 2138 XM_017013204; XM_017013211; XM_017013212; NM_001370334; XM_011517484; XM_017013203; NM_001288574; XM_017013202; NM_000503; XM_017013207; XM_017013208; XM_017013213; NM_001370336; NM_172059; NM_172060; XM_017013205; NM_172058; NM_001288575; NM_001370333; NM_001370335; XM_011517483 FSCN2 25794 NM_012418; XM_011524587; XM_011524590; XR_001752466; NM_001077182 SEMA3C 10512 NM_006379; NM_001350121; NM_001350120 MYH7 4625 XM_017021340; NM_000257 TRIP13 9319 NM_004237; XM_011514163 FGFR4 2264 NM_213647; NM_022963; NM_002011; NM_001291980; NM_001354984 CEP55 55165 XM_017016373; XM_011539920; NM_001127182; NM_018131; XM_017016372; XM_011539919; XM_011539918 TFF1 7031 NM_003225 DLGAP5 9787 XM_017021840; NM_001146015; NM_014750 BCAS1 8537 XM_005260591; XM_017028111; XM_005260595; NM_001366295; XM_005260590; XM_011529090; NM_001366298; XM_005260594; XM_005260589; XM_011529091; NM_001366297; NM_001316361; NM_003657; NM_001323347; NM_001366296 MSC 9242 NM_005098 SMR3B 10879 NM_006685 PRDM16 63976 NM_199454; NM_022114 HOXB3 3213 XM_006721854; NM_001384749; XM_024450737; XM_011524719; XM_011524720; XM_011524726; NM_001330323; XM_011524708; XM_011524721; NM_002146; XM_011524710; NM_001384747; XM_017024560; NM_001330322; NM_001384750 NNAT 4826 NM_001322802; NM_181689; NM_005386 TGFA 7039 NM_001308159; NM_001308158; NM_001099691; NM_003236 PID1 55022 NM_001330156; XM_017004404; NM_001330158; NM_017933; NM_001330157; NM_001100818 KIAA1456 57604 XM_005273591; XM_024447215; XM_005273584; XM_005273586; XM_011544600; XM_024447217; XM_005273588; XM_011544598; XM_024447214; XM_005273590; XM_017013710; NM_001099677; XM_005273585; XM_017013714; XM_011544596; XM_011544597; XM_011544601; XM_017013705; XM_024447216; XM_017013706; XM_024447218; XM_024447219; NM_020844 PAPSS2 9060 NM_001015880; NM_004670 MMRN1 22915 XM_005262856; NM_001371403; NM_007351 LYVE1 10894 NM_006691 GALE 2582 NM_000403; NM_001127621; NM_001008216 CFD 1675 NM_001317335; NM_001928 CDH3 1001 NM_001793; XM_011522800; NM_001317195; NM_001317196 TNFRSF10C 8794 NM_003841 CDKN2B 1030 NM_078487; NM_004936 BBC3 27113 XM_006723141; XM_011526722; NM_001127241; NM_001127242; NM_001127240; NM_014417 IPCEF1 26034 NM_001394801; NM_001130700; NM_015553; NM_001130699; NM_001394799; NM_001394800; NM_001394802 CDH6 1004 NM_004932; NM_001362435; XM_017008910; XM_011513921; XR_001741972 KCNJ2 3759 NM_000891 LAMB3 3914 XM_005273124; NM_001127641; XM_017001272; NM_000228; NM_001017402 E2F1 1869 NM_005225 DUSP4 1846 NM_001394; NM_057158; XM_011544428 FMO2 2327 NM_001460; NR_160266; XR_921761; NM_001365900; XR_001737072; NM_001301347 GDF15 9518 XM_024451789; NM_004864 CCL21 6366 NM_002989 PLCH1 23007 XM_011512561; XM_011512565; XM_011512566; NM_001349250; XM_011512567; XM_017005925; XM_005247239; XM_005247238; XM_011512560; XM_017005926; NM_001130960; NM_001349252; NM_014996; XM_017005927; NM_001130961; NM_001349251; XM_011512562; XM_017005923 MYOC 4653 NM_000261 GABRD 2563 XM_011541194; XM_017000936; NM_000815 TNNT3 7140 NM_001042781; NM_001363561; NM_001367847; NM_001367849; XM_006718299; XM_017018207; XM_017018208; XM_017018217; XM_024448669; XM_024448670; XM_024448671; XM_011520343; XM_017018211; XM_017018215; NM_001297646; NM_001367848; NM_001367850; XM_006718294; XM_006718300; XM_017018212; XM_017018219; NM_001042780; NM_001367845; XM_006718288; XM_017018209; XM_017018210; XM_017018218; NM_001367852; XM_017018206; XM_017018213; XM_024448672; NM_001367843; NM_001367844; NM_001367846; NM_001367851; XM_017018214; XM_017018216; NM_001042782; NM_001367842; XM_017018205; NM_006757 SLC12A5 57468 NM_020708; NM_001134771 VTCN1 79679 NM_001253849; NM_024626; NR_045604; XM_017002335; NM_001253850; NR_045603; XM_011542143 OLAH 55301 XM_024448060; XM_017016376; NM_018324; NM_001039702 MMP11 4320 NM_005940; NR_133013 AIM1L 55057 NM_017977; XM_011541672; XM_011541673; XR_001737260; NM_001039775; XR_946681; XM_005245918 CDH2 1000 XM_011525788; NM_001308176; XM_017025514; NM_001792 HAPLN1 1404 XM_017009052; XM_017009051; NM_001884; XM_017009054; XM_017009053; XM_011543168 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 ADRA2C 152 NM_000683 CD19 930 NM_001178098; NM_001385732; NM_001770; XR_950871; XM_006721103; NR_169755; XM_011545981 DHRS2 10202 NM_005794; XM_006720001; XM_005267249; NM_001318835; XR_001750107; XM_011536338; XR_001750106; XR_943366; NM_182908; XR_001750105; XR_943367; XM_011536339 Glioma AQP4 361 NM_001317387; NM_001364287; NM_001364286; NM_001317384; XM_011525942; NM_001650; NM_001364289; NM_004028 OLIG2 10215 XM_005260908; NM_005806 GFAP 2670 XM_024450691; XM_024450690; NM_001131019; XM_024450692; XM_024450693; NM_001242376; NM_002055; NM_001363846 HAPLN2 60484 XM_024448828; XM_005245415; XM_011509853; XM_017002020; XM_017002021; NM_021817 GPR37L1 9283 NM_004767; XM_011510158 PMP2 5375 NM_002677; NM_001348381 GPM6A 2823 NM_201592; NM_001261447; NM_001388091; NM_001261448; NM_005277; NR_048571; NM_001388090; NM_201591 TIMP4 7079 NM_003256 SLC1A3 6507 XM_024446182; XM_011514084; NM_004172; NM_001289940; NM_001289939; NM_001166695; XM_005248342; XM_024446181; NM_001166696 MLC1 23209 XR_001755180; NM_001376472; NM_001376478; NR_164811; NR_164812; NM_001376473; NM_001376477; NM_139202; NM_001376476; NM_001376479; NM_001376484; NM_015166; NR_164813; NM_001376474; NM_001376481; XM_011530678; NM_001376480; NM_001376483; NM_001376475; NM_001376482 NCAN 1463 NM_004386 C1orf61 10485 NM_001320454; NR_135260; NR_168070; NR_168072; NR_135267; NR_168071; NR_168073; NM_001320455; NR_135265; NR_135264; NR_135266; NM_001320453; NM_006365; NR_135268; NR_135261; NR_135262; NR_135263 CDH20 28316 XR_001753187; NM_031891; XR_001753186; XM_024451165 PTPRZ1 5803 NM_002851; NM_001206838; NM_001369396; NM_001369395; NM_001206839 MT3 4504 NM_005954 FOXG1 2290 NM_005249 DLL3 10683 NM_016941; NM_203486 KRT8 390601, 149501, 3856 NM_001256293; NM_002273 PERP 64065 XM_024446520; NM_022121 TACSTD2 4070 NM_002353 KRT7 3855 XM_017019294; XR_001748700; NM_005556; XM_011538325; XR_001748699 TES 26136 NM_015641; NM_152829; XM_005250258 EVPL 2125 NM_001988; NM_001320747 KCNK5 8645 XM_006715235; XM_005249456; NM_003740 EPCAM 4072 NM_002354 RIPK4 54101 NM_020639 SOX21 11166 NM_007084 DSP 1832 NM_001008844; NM_004415; NM_001319034 C2orf54 79919 XM_011511877; NM_001085437; NM_001282921; NM_024861 NEUROD4 58158 NM_021191 CDH1 999 NM_001317186; NM_004360; NM_001317185; NM_001317184 MASP1 5648 XM_011512989; XM_017006869; XM_017006870; XM_017006871; NM_001031849; XM_006713701; XM_011512990; NM_001879; NR_033519; XM_017006872; XM_011512991; NM_139125 CYP2C18 1562 NM_000772; NM_001128925 EPS8L1 54869 NM_133180; NM_139204; XM_011527052; XM_005259020; NM_017729; XM_011527051; XM_011527050 PDLIM1 9124 XM_011540330; NM_020992 SPINK5 11005 XM_011537551; NM_006846; NM_001127698; NM_001127699 TNNC1 7134 NM_003280 CD55 1604 NM_001300904; NM_001114543; NM_001114544; XM_017000467; NM_001114752; NM_001300902; NM_001300903; NM_000574; NR_125349 LLGL2 3993 XM_017024627; XR_002957999; XR_002958003; XM_017024626; XR_002958004; XM_017024629; XM_017024630; XM_017024631; XR_002958005; XR_002958002; NM_001015002; XM_011524802; XM_017024628; XR_002958000; XM_024450747; XR_001752508; NM_001031803; XM_017024625; XR_002958001; NM_004524 ITPR3 3710 XM_017010832; XM_011514577; NM_002224 SPINT2 10653 NM_001166103; NM_021102 ANXA3 306 XR_001741215; NM_005139 HCN2 610 NM_001194 F2R 2149 NM_001311313; NM_001992 MYL2 4633 NM_000432 KIFC1 3833 XM_011514585; XM_017010836; NM_002263; XM_011514587; XM_017010837 BIRC5 332 NM_001168; NM_001012271; NM_001012270 NDC80 10403 NM_006101 PBK 55872 NM_018492; NM_001278945; NM_001363040 TACC3 10460 XM_005247930; XM_017007653; NM_006342; XM_005247929; XM_011513386 EGFR 1956 NM_001346899; NM_201282; NM_201284; NM_001346898; NM_001346900; NM_001346897; NM_201283; NM_001346941; NM_005228 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 Sarcoma RAB11FIP1 80223 NM_001002814; NM_025151; XM_017013869; NM_001002233 LOXL1 4016 XM_017022179; XM_011521555; NM_005576; XR_931824 ZNF385D 79750 XM_017007203; NM_024697; XM_017007200; XM_011534124; XM_017007195; XM_017007202; XM_017007193; XM_017007197; XM_011534122; XM_017007191; XM_017007192; XM_017007199; XM_017007201; XM_024453754; XM_011534123; XM_017007194; XM_017007196; XM_017007198 MYL2 4633 NM_000432 AGRN 375790 XM_011541429; NM_001305275; NM_001364727; XR_946650; NM_198576; XM_005244749 KCNG1 3755 XM_011528800; XM_011528802; XM_011528803; XM_011528805; NM_172318; NM_002237; XM_011528801; XM_011528804; XM_011528806; XM_006723785 NKX3-2 579 NM_001189 NXPH3 11248 NM_007225 HMX1 3166 NM_018942; NM_001306142 CLDN7 1366 NM_001307; NM_001185022; NM_001185023 TUBB4A 10382 NM_001289129; NM_001289131; NM_006087; NM_001289123; NM_001289127; NM_001289130 RAB17 64284 XM_006712689; XM_017004693; NM_022449; XM_017004694; NR_033308 EPCAM 4072 NM_002354 GH1 2688 NM_022559; NM_022561; NM_022560; NM_022562; NM_000515 ERBB3 2065 NM_001005915; NM_001982 ELMO3 79767 XM_024450447; NM_024712 SYNC 81493 XM_024450011; NM_001161708; XM_024450013; NM_030786; XM_024450012; XM_024450010; XM_024450014 TPD52 7163 NM_005079; NR_105035; NM_001287143; NM_001387779; NR_105037; NR_170694; NM_001025252; NM_001025253; NR_170693; NM_001287140; NR_105034; NM_001387780; NM_001287142; NM_001287144; NM_001387778; NR_105033; NR_105036 S100B 6285 NM_006272; XM_017028424 PALMD 54873 NM_017734 CYP46A1 10858 NM_006668; XM_005267274; XM_011536365; XM_011536364; XM_017020933 PNPLA2 57104 NM_020376 SERINC2 347735 NM_178865; NM_001199039; NM_018565; NM_001199038; NM_001199037 PRSS12 8492 XM_011532387; NM_003619; XM_005263318 OLR1 4973 NM_002543; NM_001172632; NM_001172633 TNNT3 7140 NM_001042781; NM_001363561; NM_001367847; NM_001367849; XM_006718299; XM_017018207; XM_017018208; XM_017018217; XM_024448669; XM_024448670; XM_024448671; XM_011520343; XM_017018211; XM_017018215; NM_001297646; NM_001367848; NM_001367850; XM_006718294; XM_006718300; XM_017018212; XM_017018219; NM_001042780; NM_001367845; XM_006718288; XM_017018209; XM_017018210; XM_017018218; NM_001367852; XM_017018206; XM_017018213; XM_024448672; NM_001367843; NM_001367844; NM_001367846; NM_001367851; XM_017018214; XM_017018216; NM_001042782; NM_001367842; XM_017018205; NM_006757 HOOK1 51361 XR_946665; XM_017001424; XM_006710676; XR_246271; XM_011541563; XM_024447520; XM_011541562; NM_015888 GDPD3 79153 NM_024307 EPM2A 7957 NM_001368131; XM_017011301; NM_001360057; NM_001360064; NM_001368129; XM_024446550; XM_011536113; NM_001368130; NM_005670; NR_153398; XM_017011302; XM_011536116; NM_001360071; NM_001018041; XM_024446551; NM_001368132 C1orf116 79098 XM_011509973; NM_001083924; XM_005273259; XM_006711530; NM_023938 CCDC68 80323 XM_011526201; XM_017026011; XM_011526198; XM_006722552; NM_001143829; XM_011526199; XM_011526203; XM_011526204; NM_025214; XM_011526200; XM_011526202 VGF 7425 NM_003378; XM_011516549; XM_005250561 PLEK2 26499 NM_016445 FBN2 2201 NM_001999; XM_017009228 FGF7 2252 NM_002009 RCN3 57333 NM_020650; XM_024451620 FBXO2 26232 NM_012168 COX7A1 1346 NM_001864 EBF2 64641 NM_022659 ADAMTS2 9509 NM_021599; NM_014244 TAGEN3 29114 NM_001008272; NM_001008273; NM_013259 HAND2 9464 NM_021973 MT3 4504 NM_005954 RAP1GAP 5909 XR_001737354; XR_001737351; NM_001145657; NM_001350527; NM_001350528; NM_001388217; NM_001388229; NM_001388241; NM_001388254; NM_001388259; NM_001388263; NM_001388266; NM_001388267; NM_001388276; NM_001388285; NM_001388287; NM_001388290; NM_001388294; NM_001388295; NR_170904; NR_170911; NR_170915; NR_170920; NR_170928; XR_001737352; XR_946730; NM_001145658; NM_001330383; NM_001388205; NM_001388211; NM_001388216; NM_001388221; NM_001388224; NM_001388227; NM_001388239; NM_001388245; NM_001388280; NM_001388281; NR_170900; NR_170923; NR_170927; NM_001350526; NM_001388222; NM_001388243; NM_001388252; NM_001388256; NM_001388258; NM_001388261; XR_946728; NM_001388203; NM_001388209; NM_001388206; NM_001388230; NM_001388231; NM_001388240; NM_001388242; NM_001388247; NM_001388253; NM_001388255; NM_001388288; NM_001388289; NM_001388296; NR_170907; NR_170909; XR_001737349; NM_001350525; NM_001388204; NM_001388207; NM_001388210; NM_001388219; NM_001388220; NM_001388228; NM_001388233; NM_001388235; NM_001388236; NM_001388238; NM_001388248; NM_001388284; NM_001388286; NR_170910; NR_170924; NM_001388202; NM_001388208; NM_001388214; NM_001388218; NM_001388234; NM_001388249; NM_001388270; NM_001388279; NM_002885; NR_170901; NR_170902; NR_170903; NR_170912; NR_170913; NR_170926; XR_946726; NM_001350524; NM_001388200; NM_001388212; NM_001388213; NM_001388215; NM_001388225; NM_001388226; NM_001388244; NM_001388246; NM_001388251; NM_001388282; NM_001388283; NR_170908; NR_170914; NR_170921; NR_170925; NM_001388201; NM_001388223; NM_001388237; NM_001388250; NM_001388264; NM_001388269; NM_001388273; NM_001388291; NM_001388292; NM_001388293 GAS1 2619 NM_002048 CDKL2 8999 XR_001741344; XR_001741345; XM_017008811; XM_017008810; XM_006714406; NM_003948; XM_017008809; NM_001330724 SCN4A 6329 NM_000334 COL5A1 1289 NM_000093; XM_017014266; XR_001746183; NM_001278074 WWC1 23286 XM_011534487; XM_011534489; NM_015238; XM_005265850; XM_011534485; XM_011534486; XM_005265853; XM_011534488; XM_011534490; XM_011534491; XM_017009276; XM_017009278; NM_001161662; NM_001161661 POPDC2 64091 NM_001369919; NM_022135; NM_001308333 TFAP2A 7020 NM_001032280; XM_006715175; NM_001042425; XM_017011232; XM_011514833; NM_001372066; NM_003220 EN1 2019 NM_001426 CHRD 8646 XM_017007390; NR_130747; NM_177978; XM_017007388; XM_017007391; XM_024453803; XR_001740336; NM_001304472; XM_017007392; XR_001740334; XM_011513254; XR_002959603; NM_001304473; NM_177979; NM_001304474; NM_003741; XM_017007389; XM_017007393; XM_017007394; XR_001740335; XR_001740337 PLS1 5357 NM_001172312; XM_011512901; NM_001145319; XM_006713660; XM_017006626; XM_011512903; XM_017006627; XM_011512900; NM_002670 ELF3 1999 NM_004433; XM_005244942; NM_001114309 DBNDD1 79007 NM_001288709; NM_001288708; NM_001371581; NM_001042610; NM_024043 RAB23 51715 NM_183227; NM_001278666; NM_001278668; NM_016277; NM_001278667; NR_103822 CD24 100133941 NM_001291739; NR_117090; NR_117089; NM_001291738; NM_001291737; NM_013230; XM_024446293; NM_001359084 SLC43A1 8501 XM_017018453; XM_024448727; XM_011545322; XM_011545321; XM_017018452; XM_011545320; XM_024448728; NM_001198810; XM_005274358; XM_017018451; NM_003627 AMPH 273 XM_006715689; XM_017011996; XM_006715690; XM_006715691; XM_011515271; XM_017011995; NM_001635; NM_139316 KRT8 390601, 149501, 3856 NM_001256293; NM_002273 HOXA7 3204 NM_006896 CORO2A 7464 NM_003389; NM_052820; XM_011518986 RNF43 54894 XM_011524955; XM_011524956; NM_017763; NM_001305544; XM_017024800; NM_001305545 PER1 5187 XM_005256689; XM_005256690; XM_024450803; NM_002616 SHOX2 6474 XM_006713727; NM_001163678; XM_017007055; NM_006884; XM_006713728; XM_017007053; NM_003030; XM_017007054 MYRF 745 NM_013279; XM_005274222; XM_005274224; XM_005274226; XM_005274228; XM_005274223; XM_005274225; XM_005274227; XM_011545234; XM_024448677; NM_001127392 GPR1 2825 NM_001098199; NM_001261452; NM_001261454; NM_005279; XM_005246471; NM_001261455; NM_001389445; NM_001261453 CIDEC 63924 NM_001321142; NM_001199552; NM_001378491; NM_001199623; NM_001199551; NM_001321144; NM_022094; NM_001321143 APOD 347 NM_001647 KRT2 3849 NM_000423 HOXD9 3235 NM_014213 KCNB2 9312 XM_017013981; XR_001745620; XR_001745621; NM_004770; XM_017013982 FABP6 2172 NM_001130958; NM_001040442; NM_001445 CCNB1 891 NM_031966 DSP 1832 NM_001008844; NM_004415; NM_001319034 KRT5 3852 NM_000424 LGI2 55203 XM_011513850; NM_018176; XM_017008356 CKM 1158 NM_001824 ITGB4 3691 XM_005257311; XM_006721866; XM_006721870; NM_000213; NM_001005619; NM_001005731; XM_005257309; XM_011524752; XM_006721867; XM_011524751; NM_001321123; XM_006721868 AP1M2 10053 NM_001300887; XM_024451304; NM_005498; XM_024451303 QPRT 23475 XM_005255223; NR_134536; NM_001318250; NM_001318249; NM_014298; XM_017023101 FOXD1 2297 NM_004472 NPPA 4878 NM_006172 DDR2 4921 NM_001014796; XM_011509587; XM_011509588; NM_001354982; NM_006182; NM_001354983 PFKFB1 5207 NM_001271804; XM_017029578; XM_017029576; NM_002625; NR_073450; XM_024452389; XM_017029577; NM_001271805 BNC2 54796 NM_001317939; NM_017637; NM_001317940 PCOLCE 5118 XM_024446785; NM_002593 GIPC2 54810 NM_017655; NM_001304725 FZD2 2535 NM_001466 COL1A2 1278 NM_000089 FST 10468 XM_005248403; XM_011543099; XM_005248400; XM_017008955; NM_013409; XM_005248401; XM_005248402; XM_017008954; XM_024454326; NM_006350 BIK 638 NM_001197 C1QL1 10882 NM_006688 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 MYOC 4653 NM_000261 GABRQ 55879 NM_018558; XM_011531184 SCN5A 6331 NM_001160160; NM_001099405; NM_001354701; XM_011533991; XM_017007017; NM_001160161; NM_198056; NM_000335; NM_001099404 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 Neuroendocrine CA7 766 NM_001365337; XM_011523312; NM_001014435; NM_005182 TGM3 7053 NM_003245 HLA-G 3135 XM_017010817; NM_001384280; XM_017010818; NM_002127; XM_024446420; NM_001363567; NM_001384290 MYL2 4633 NM_000432 CCNB1 891 NM_031966 UPK3A 7380 NM_006953; NM_001167574 LYVE1 10894 NM_006691 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 PLA2G1B 5319 NM_000928 DBNDD1 79007 NM_001288709; NM_001288708; NM_001371581; NM_001042610; NM_024043 MET 4233 NM_001324402; NM_001324401; XM_006715990; NM_001127500; XM_011516223; NM_000245; XR_001744772; ESM1 11082 NM_001135604; NM_007036 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 KRT2 3849 NM_000423 HRASLS2 54979 NM_017878; XM_011545120 DGKI 9162 NM_004717; NM_001321708; XM_017012788; NM_001321710; NM_001388092; NM_001321709 SLC18A1 6570 XM_011544626; NM_003053; XM_011544625; NM_001142325; NM_001135691; NM_001142324 MMP11 4320 NM_005940; NR_133013 FIGF 2277 NM_004469 SLC16A7 9194 XM_011538990; XM_011538992; NM_004731; NM_001270622; XM_017020225; XM_017020227; NR_073055; XM_011538989; NM_001270623; XM_024449276; XM_011538991; XM_011538993; NR_073056; XM_005269231; XM_011538995; XM_017020226; XM_017020224 VIP 7432 XM_006715562; XM_005267135; NM_003381; NM_194435 CD200 4345 NM_001318830; NR_158642; NM_001004197; NM_001365853; NM_001365855; NM_001318826; NM_001365852; NM_001004196; NM_001318828; NM_001365851; NM_005944; NM_001365854 SLITRK3 22865 NM_014926; NM_001318810; NM_001318811 FCN2 2220 XM_011518392; NM_015838; NM_015839; NM_015837; NM_004108; XM_006717015 MT3 4504 NM_005954 ADRB2 154 NM_000024 CACNG4 27092 NM_014405 SYNPO2L 79933 NM_024875; NM_001114133; XM_005270159; XM_005270158 VILL 50853 NM_001370265; NR_163266; NR_163267; NM_001370264; NM_015873; NM_001385039; NM_001385038 AGRN 375790 XM_011541429; NM_001305275; NM_001364727; XR_946650; NM_198576; XM_005244749 CYP11B1 1584 NM_001026213; NM_000497 EPHB3 2049 NM_004443 KCNMB1 3779 NM_004137 ADAMTS13 11093 NM_139027; NM_139028; XM_017014235; NM_139026; XM_017014233; XR_001746171; NM_139025; XM_017014232; XM_011518176; XM_017014234; XM_011518178; XM_011518179; NR_024514 SCGB2A1 4246 NM_002407 ABCC4 10257 XM_017020321; NM_001301829; NM_005845; XM_005254025; XM_017020319; NM_001301830; NM_001105515; XM_017020322; XM_017020320 CRNN 49860 NM_016190 CHGB 1114 NM_001819 HIGD1B 51751 XM_011524891; NM_016438; XM_006721946; XM_006721947; XM_017024742; NR_073504; XM_006721948; XM_017024743; NM_001271880 CELA2A 63036 NM_033440 CLPS 1208 NM_001832; NM_001252597; NM_001252598 HECW1 23072 XM_006715670; XM_006715671; XM_011515225; XM_017011882; XM_011515220; XM_011515223; XM_017011886; XM_017011888; NM_001287059; NM_015052; XM_017011883; XM_006715673; XM_011515222; XM_011515224; XM_017011884; XM_017011889; XM_017011885; XM_017011887; XM_011515226; XM_017011890; XM_005249665 ERBB3 2065 NM_001005915; NM_001982 PPY 5539 NM_002722; NM_001319209; XM_011524978 CKM 1158 NM_001824 CXorf36 79742 XM_006724559; NM_176819; NM_024689; XM_005272670 HOXA10 3206 NR_037939; NM_153715; NM_018951 RIBC2 26150 XM_005261524; XM_011530126; NM_015653; XM_017028766 CDH19 28513 XM_011525931.3; XM_017025711.2; XM_011525932.1 SLC24A2 25769 XM_017014592; NM_001193288; NM_001375850; NM_020344; NM_001375851 ADAMDEC1 27299 NM_001145272; NM_014479; NM_001145271; NR_156422 MMP28 79148 XM_017025061; XM_017025062; NM_024302; XM_011525227; NM_001032278; NM_032950; XM_011525228; XM_011525225; XM_011525230; XM_024450943; XM_011525226; NR_111988; XM_011525229; XM_011525231; XM_011525232; XM_017025063; XM_017025064 KRT17 3872 NM_000422 S100P 6286 NM_005980 NOX4 50507 NM_001291926; XM_006718849; NM_016931; NM_001143837; XM_011542857; NM_001143836; NM_001291927; XM_017017842; XM_017017843; XM_017017844; XM_017017841; XM_017017845; NM_001291929; NM_001300995; NR_120406 CELSR1 9620 XM_011530554; XM_011530555; NM_001378328; XM_011530553; NM_014246 CPB1 1360 NM_001871 CCL23 6368 NM_005064; XR_429910; NM_145898 CELA3A 10136 NM_005747 WISP2 8839 NM_001323369; XM_017028116; NM_003881; XM_017028117; NM_001323370 GCG 2641 NM_002054 CACNA1H 8912 XM_006720965; XM_017023820; XM_006720963; XM_006720967; XM_011522724; XR_002957850; XM_005255652; XM_017023821; XM_011522727; XM_017023819; NM_021098; XM_006720968; XM_006720964; NM_001005407 PDX1 3651 NM_000209; XR_941580; XR_941578; FABP7 2173 NM_001319039; NM_001319041; NM_001319042; NM_001446 NRTN 4902 NM_004558 NMB 4828 XM_017022239; NM_021077; NM_205858 AMHR2 269 XM_011538179; XM_011538184; XM_017019179; NM_020547; XR_002957309; XR_002957311; XM_011538178; XM_011538176; XM_011538181; XM_011538185; NM_001164691; XM_011538174; XM_011538183; XR_002957310; XM_011538186; XR_002957312; NM_001164690; XM_011538173; XM_011538180; XM_024448938 WNT2 7472 NM_003391; NR_024047 GFAP 2670 XM_024450691; XM_024450690; NM_001131019; XM_024450692; XM_024450693; NM_001242376; NM_002055; NM_001363846 CYP11B2 1585 NM_000498 SGCA 6442 XM_011525122; XM_011525120; XM_011525121; XM_024450873; NM_001135697; NR_135553; XR_002958056; XM_011525124; NM_000023; XM_011525123 PNMA2 10687 NM_007257; XM_011544365 CEL 1056 NM_001807 MT1M 4499 NM_176870 CST1 1469 NM_001898 ITPKB 3707 NM_002221; NM_001388404; XM_017001211 ALAS2 212 NM_001037968; NM_001037967; NM_000032; NM_001037969 INS 3630 NM_001185098; NM_001185097; NM_000207; NM_001291897 LGALS4 3960 NM_006149; XM_011526974; XM_011526973 PLP1 5354 NM_001128834; NM_000533; NM_001305004; NM_199478 GABRQ 55879 NM_018558; XM_011531184 PLAG1 5324 XM_017013576; XM_017013577; NM_001114635; XM_011517544; NM_001114634; NM_002655 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 CYP11A1 1583 NM_000781; NM_001099773 FABP1 2168 NM_001443 S100A12 6283 NM_005621 IL20RA 53832 NM_001278722; XM_011535904; XM_017010955; NM_001278724; NM_014432; XM_006715506; NM_001278723; XM_017010954 NR5A1 2516 NM_004959 BCAS1 8537 XM_005260591; XM_017028111; XM_005260595; NM_001366295; XM_005260590; XM_011529090; NM_001366298; XM_005260594; XM_005260589; XM_011529091; NM_001366297; NM_001316361; NM_003657; NM_001323347; NM_001366296 ERBB2 2064 XM_024450643; NM_001005862; NM_001382784; NM_001382785; NM_001382788; NM_001382792; NM_001382793; NM_001382803; NM_001289937; NM_001382786; NM_001382800; NM_001382802; NM_001382806; XM_024450641; NM_001382782; NM_001382789; NM_001382795; NM_001289936; NM_001382797; NM_001382805; NM_004448; NR_110535; XM_024450642; NM_001289938; NM_001382791; NM_001382801; NM_001382783; NM_001382790; NM_001382794; NM_001382798; NM_001382799; NM_001382787; NM_001382796; NM_001382804 SLC12A3 6559 NM_000339; NM_001126108; NM_001126107; XM_005256119 GRHL2 79977 XM_011517306; XM_024447286; NM_001330593; NM_024915; XM_011517307 HBB 3043 NM_000518 C7 730 NM_000587 MOGAT2 80168 XM_024448696; NM_025098; XM_011545267 MYOC 4653 NM_000261 TP73 7161 NM_001126242; NM_001204191; NM_001126240; NM_001204185; NM_001204187; NM_001204184; NM_001204186; NM_001204192; NM_001126241; NM_001204190; NM_001204188; NM_001204189; NM_005427 EPS8L3 79574 XM_017002329; XM_011542135; XM_011542134; NM_139053; NM_001319952; NM_024526; XM_011542133; XM_017002328; XR_001737407; XM_017002327; NM_133181; XM_011542132; XR_001737406 BCAM 4059 NM_001013257; NM_005581 KHDC1L 100129128 NM_001126063 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 CXCR2 3579 XM_017003992; XM_017003990; NM_001168298; NM_001557; XM_005246530; XM_017003991 KRT24 192666 XM_017024299; NM_019016; XM_006721739; XM_011524460 SIX1 6495 XM_017021602; NM_005982 PTPRH 5794 XM_011527188; XM_017027061; NM_001161440; XM_017027058; XR_001753731; XM_017027056; XM_017027062; XM_017027059; XM_011527183; XR_001753730; XM_017027063; XM_017027064; XM_011527190; XM_017027057; XM_017027060; NM_002842 TNXB 7148 NM_001365276; NM_019105; NM_032470 SLC6A7 6534 XR_001742210; XM_024446190; XR_001742212; XM_017009770; XR_001742211; XM_017009767; XM_017009769; XM_017009768; NM_014228 PLAGL1 5325 NM_001289037; NM_001289040; NM_001289046; NM_001289047; NM_001317157; NM_001080956; NM_001080951; NM_001080955; NM_001289044; NM_001289048; NM_001289049; NM_001317159; NM_001317162; NM_001289038; NM_001080953; NM_001080954; NM_001289043; NM_001317156; NM_001317158; NM_001080952; NM_001289041; NM_001289045; NM_001317161; NM_002656; NM_006718; NM_001289039; NM_001289042; NM_001317160 ADH1B 125 NM_001286650; NM_000668 FSTL4 23105 XM_011543284; XM_011543286; XM_011543287; XM_011543283; XM_017009251; NM_015082 MFAP2 4237 NM_002403; NM_017459; NM_001135247; NM_001135248 TREM2 54209 NM_001271821; NM_018965 COL1A2 1278 NM_000089 LRP2 4036 XM_011511183; NM_004525; XM_011511184 CDK1 983 NM_001320918; NM_033379; NM_001170406; NM_001786; NM_001130829; XM_005270303; NM_001170407 EBF2 64641 NM_022659 CDH3 1001 NM_001793; XM_011522800; NM_001317195; NM_001317196 SVEP1 79987 NM_024500; NM_153366 CNNM1 26507 NM_001345888; XM_011539631; XR_002956974; NM_020348; NM_001345887; NM_001345889; NR_144311; XR_945667 TLN2 83660 XM_017022669; XM_005254713; XM_005254715; XM_006720717; XM_017022667; XM_005254714; XM_005254708; XM_005254710; XR_001751405; NM_001394547; XM_005254712; NM_015059; XM_017022666; XM_024450087; XM_005254711; XM_017022665; XM_017022668 ADAM12 8038 XM_017016705; NM_001288973; NM_001288974; NM_001288975; XM_017016706; NM_003474; NM_021641; XM_024448210 MAGEA1 4100 NM_004988 Pheochromocytoma PHOX2A 401 NM_005169 DDC 1644 XM_011515161; NM_001242890; XM_005271745; NM_001082971; NM_001242886; NM_001242887; NM_001242889; NM_000790; NM_001242888 INSM1 3642 NM_002196 CYP11A1 1583 NM_000781; NM_001099773 SYT5 6861 XM_006723339; NM_001297774; NM_003180; XM_017027175; XM_006723340; XM_006723341; XM_024451668 NGB 58157 NM_021257 STAR 6770 NM_001007243; NM_000349 SLC18A1 6570 XM_011544626; NM_003053; XM_011544625; NM_001142325; NM_001135691; NM_001142324 CHGB 1114 NM_001819 CHRNA3 1136 XM_006720382; NM_000743; NR_046313; NM_001166694 CHGA 1113 NM_001301690; NM_001275; XM_011536370 SLC18A2 6571 NM_003054 DBH 1621 NM_000787 DRD2 1813 XM_017017296; NM_016574; NM_000795 TH 7054 XM_011520335; NM_199292; NM_000360; NM_199293 PPP1R17 10842 XR_926912; NM_001145123; XM_011515094; NM_006658 PHOX2B 8929 NM_003924 EGR4 1961 NM_001965 CDH22 64405 XM_024451966; XM_011528994; XM_024451967; NM_021248 SFN 2810 NM_006142 C1orf106 55765 XM_011509754; XM_011509755; NM_001367289; NM_001367290; XM_011509756; NM_001142569; NM_018265 CDC20 991 NM_001255 TGFA 7039 NM_001308159; NM_001308158; NM_001099691; NM_003236 SMO 6608 NM_005631; XM_024446891 SDC1 6382 NM_001006946; XM_005262620; XM_005262621; NM_002997; XM_005262622 VAMP8 8673 NM_003761; XM_017005170 SERPINA1 5265 NM_001002235; NM_001127700; NM_001127701; XM_017021370; NM_001127706; NM_000295; NM_001002236; NM_001127702; NM_001127705; NM_001127703; NM_001127704; NM_001127707 EPHB3 2049 NM_004443 BIRC5 332 NM_001168; NM_001012271; NM_001012270 CILP 8483 NM_003613; XM_017022679; XM_017022678 ABAT 18 NM_001386601; NM_001386602; NM_001386615; NM_000663; NM_001386606; NM_001127448; NM_020686; NM_001386608; NM_001386612; NM_001386613; NM_001386603; NM_001386605; NM_001386611; NM_001386600; NM_001386609; NM_001386610; NM_001386614; NM_001386616; NM_001386604; NM_001386607 CSTA 1475 NM_005213 PRUNE2 158471 XM_011518327; XM_005251746; XM_005251751; XM_006716983; XM_017014347; XM_017014349; XM_017014359; XR_001746209; XR_428517; XM_005251748; XM_006716985; NM_001308047; XM_005251754; XM_006716982; XM_017014346; XM_017014348; XM_017014352; XR_001746210; NM_001308050; NR_131751; NM_138818; XM_011518323; XM_017014345; XM_017014357; XR_001746212; NM_001308048; NM_015225; XM_017014354; XM_017014356; NM_001308049; XM_005251750; XM_005251745; XM_006716986; XM_011518326; XM_011518328; XM_017014350; XM_017014351; XM_017014353; XM_017014358; XM_006716984; XR_001746211; NM_001308051; NM_001330680 WNT2 7472 NM_003391; NR_024047 UGT2A3 79799 XM_011532247; NM_024743; NR_024010 IRS4 8471 XM_006724713; NM_003604; NM_001379150; XM_011531061 SLC6A15 55117 XM_011538525; NM_018057; NM_001146335; NM_182767 ATP2B2 491 XM_017006484; NM_001001331; XM_005265179; XM_011533752; XM_017006487; XM_017006488; XM_017006486; XM_017006481; XM_017006482; XM_017006489; XM_006713175; NM_001330611; NM_001353564; XM_017006485; XM_017006483; NM_001683; XM_017006492; NM_001363862 WWC1 23286 XM_011534487; XM_011534489; NM_015238; XM_005265850; XM_011534485; XM_011534486; XM_005265853; XM_011534488; XM_011534490; XM_011534491; XM_017009276; XM_017009278; NM_001161662; NM_001161661 FCN2 2220 XM_011518392; NM_015838; NM_015839; NM_015837; NM_004108; XM_006717015 IVL 3713 NM_005547 CFTR 1080 NM_000492 F2RL1 2150 NM_005242; XM_017009223 MYB 4602 NM_001161660; NR_134958; NM_001130173; NM_001130172; NM_001161656; NR_134959; NM_001161657; NR_134963; NR_134965; XR_942444; NR_134962; NM_001161659; NR_134961; NM_001161658; NM_005375; NR_134960; NR_134964 SCGN 10590 NM_006998; XM_017010181 TMEM246 84302 NM_001303107; NM_001303108; NM_032342; XM_024447701; NM_001371233 PRSS22 64063 XM_005255473; NM_022119 IHH 3549 NM_002181 MYBPH 4608 NM_004997 SPOCK2 9806 XM_017016985; NM_001134434; XM_011540404; NM_001244950; NM_014767 MUC2 4583 NM_002457 MYCL 4610 NM_001033082; NM_001033081; NM_005376 Mesothelioma CPA4 51200 NM_001163446; NM_016352 CALB2 794 NM_007088; XR_002957842; NM_001740; NR_027910; NM_007087 HAS1 3036 NM_001523; NM_001297436; XM_011526884 ALDH1A2 8854 NM_001206897; NM_170697; NM_170696; NM_003888 PTGIS 5740 NM_000961 UPK1B 7348 NM_006952 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 MYL2 4633 NM_000432 HP 3240 NM_001126102; NM_005143; NM_001318138 MSLN 10232 NM_001177355; NM_005823; NM_013404 GJB1 2705 NM_000166; XM_011530907; NM_001097642 CKM 1158 NM_001824 TM4SF1 4071 NM_014220; XM_017006385 CST1 1469 NM_001898 CTSE 1510 XM_011509245; NM_001910; NM_148964; XM_011509244; NM_001317331 SLC44A4 80736 NM_001178045; NM_001178044; NM_025257 CD24 100133941 NM_001291739; NR_117090; NR_117089; NM_001291738; NM_001291737; NM_013230; XM_024446293; NM_001359084 BMP7 655 NM_001719 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 GATA4 2626 NM_001308093; NM_002052; NM_001308094; NM_001374273; NM_001374274 IRF6 3664 NM_001206696; NM_006147 KRT5 3852 NM_000424 PRSS22 64063 XM_005255473; NM_022119 CLIC3 9022 XM_017015282; NM_004669; XM_017015281 FLNC 2318 NM_001458; NM_001127487 SALL1 6299 NM_001127892; NM_002968 ERBB3 2065 NM_001005915; NM_001982 TF 7018 NM_001063; NM_001354703; NM_001354704 GJB3 2707 NM_024009; NM_001005752 BDNF 627 NM_001143811; NM_001143812; NM_170734; XM_011520280; NM_001143805; NM_001143816; NM_170731; NM_001143808; NM_001143809; NM_001143814; NM_001143815; NM_001143807; NM_001709; NM_001143810; NM_001143813; NM_170732; NM_001143806; NM_170733; NM_170735 ADRA2B 151 NM_000682 TPO 7173 XM_024453088; XM_024453087; NM_175722; XM_024453091; XM_024453085; XM_024453086; NM_001206745; XM_024453090; NM_175719; NM_175721; NM_175720; XM_024453093; XM_011510380; NM_001206744; XM_024453089; XM_024453092; NM_000547 CENPF 1063 XM_017000086; NM_016343; XM_011509082 SCN4A 6329 NM_000334 KRT18 3875 NM_000224; NM_199187 SPINT2 10653 NM_001166103; NM_021102 KIF4A 24137 NM_012310 DHRS2 10202 NM_005794; XM_006720001; XM_005267249; NM_001318835; XR_001750107; XM_011536338; XR_001750106; XR_943366; NM_182908; XR_001750105; XR_943367; XM_011536339 SDC1 6382 NM_001006946; XM_005262620; XM_005262621; NM_002997; XM_005262622 ROBO3 64221 NM_001370358; NM_001370359; NR_163412; NM_001370356; NM_001370361; NR_163411; NR_163415; NM_001370364; NM_022370; NR_163410; NR_163413; NR_163414; XM_017018122; NM_001370366; NM_001370357; NR_163409 FHL5 9457 NM_001170807; NM_001322466; NM_001322467; NM_020482 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 PKMYT1 9088 NM_001258451; NM_182687; NM_001258450; XM_011522735; XM_024450490; NM_004203; XM_011522734; XM_011522736 NEIL3 55247 NM_018248; XM_017008360 PHKG1 5260 NM_001258460; XM_017012327; XM_017012324; XM_017012325; NR_047689; XM_017012326; NM_001258459; XM_005271772; NM_006213 KRT2 3849 NM_000423 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 SEMA6C 10500 NM_030913; XM_017000075; XM_017000079; NM_001178061; NM_001178062; XM_017000077; XM_017000082; XM_017000076; XM_017000081; XM_005244835 CIDEC 63924 NM_001321142; NM_001199552; NM_001378491; NM_001199623; NM_001199551; NM_001321144; NM_022094; NM_001321143 SPANXB1 728695 NM_145664; NM_032461 GH1 2688 NM_022559; NM_022561; NM_022560; NM_022562; NM_000515 PLIN1 5346 NM_002666; XM_005254934; NM_001145311 PPARG 5468 NM_001354669; NM_001354670; NM_001374263; NM_001330615; NM_001374262; NM_005037; NM_001374261; NM_138711; NM_138712; NM_001374264; NM_001374266; NM_001354668; NM_015869; NM_001354667; NM_001354666; NM_001374265 CACNA1S 779 XM_005245478; NM_000069 Thymoma MAOB 4129 XM_005272608; XM_017029524; XM_017029523; NM_000898 ANKS1B 56899 XM_006719507; XM_024449067; NM_001204070; NM_001352193; NM_001352198; NM_001352201; NM_001352207; NM_001352219; NM_001352221; XM_006719508; XM_017019654; XM_024449061; XM_024449062; NM_001204065; NM_001352185; NM_001352191; NM_001352194; NM_001352202; NM_001352203; NM_001352209; NM_001352211; NM_001352213; NM_001352220; XM_017019655; XM_024449069; NM_001204068; NM_001352205; NM_001352214; NM_001352216; NM_001352218; NM_001352223; NM_001352225; NM_020140; XM_024449063; XM_024449066; XM_024449070; NM_001204066; NM_001352186; NM_001352187; NM_001352195; NM_001352200; NM_001352212; NM_152788; XM_005269029; XM_006719505; XM_006719510; XM_006719512; XM_011538571; XM_017019656; XM_024449065; NM_001204079; NM_001352189; NM_001352190; NM_001352197; NM_001352222; XM_006719513; XM_006719514; XM_017019652; XM_024449064; XR_001748815; NM_001204069; NM_001204067; NM_001204081; NM_001352199; NM_001352204; NM_001352206; NM_001352210; NM_001352217; NM_181670; XM_017019653; NM_001352196; XM_006719504; XM_017019657; XM_017019658; XM_024449060; XM_024449068; NM_001204080; NM_001352188; NM_001352192; NM_001352208; NM_001352224 SPINK2 6691 XM_024454191; XM_011534405; NM_001271718; NM_001271720; NM_001271721; NR_073417; NM_001271719; XM_011534406; NM_001271722; NM_021114; NR_073418; NR_073419 KREMEN2 79412 NM_145348; NM_145347; NM_024507; NM_172229; NM_001253726; NM_001253725 ORC1 4998 NM_001190818; XM_017001388; XM_017001389; NM_001190819; XM_011541527; NM_004153 GJB1 2705 NM_000166; XM_011530907; NM_001097642 DPF1 8193 XM_006723408; XR_243964; XM_011527356; XM_024451731; NM_004647; XM_005259292; XM_006723407; NM_001135155; XM_006723409; XM_006723410; XM_011527358; NM_001363579; XM_011527357; XM_005259289; NM_001135156; NM_001289978 PAX1 5075 NM_006192; NM_001257096 FCN2 2220 XM_011518392; NM_015838; NM_015839; NM_015837; NM_004108; XM_006717015 KIFC1 3833 XM_011514585; XM_017010836; NM_002263; XM_011514587; XM_017010837 RAG1 5896 NM_001377278; NM_000448; NM_001377280; NM_001377277; NM_001377279 FOXN1 8456 XM_011525358; XM_011525362; XM_011525359; XM_011525367; XM_011525368; XM_011525370; XM_017025230; XM_017025231; XM_017025229; XM_011525369; XM_017025228; NM_001369369; NM_003593 ZAP70 7535 XM_017004868; XR_001738927; NM_001378594; NM_207519; XM_017004869; NM_001079; XR_001738926; XM_017004870; XM_017004867; XR_001738925 PCDH1 5097 XM_005268455; NM_001278613; XM_005268452; XM_017009517; NM_032420; NM_002587; XM_005268454; XM_017009518; NM_001278615 LCK 3932 XM_011541453; XM_024447046; NM_001330468; XM_024447047; NM_005356; NM_001042771 MLANA 2315 NM_005511 KRT5 3852 NM_000424 NDRG2 57447 NM_016250; NM_001354567; NM_201538; NM_001282215; NM_001354560; NM_001354561; NM_001354569; NM_201535; NM_001282216; NM_001354564; NM_001354565; NM_001354566; NM_201536; NM_201539; NM_201541; NM_001354558; NM_001354562; NM_001282213; NM_001354570; NM_201540; NM_001282211; NM_001320329; NM_001282214; NM_001282212; NM_001354559; NM_001354568; NM_201537 GFI1B 8328 NM_001371908; NM_001377304; XM_006717297; NM_001135031; XM_017015175; NM_001377305; XM_011519069; XM_011519070; NM_004188; XM_011519068; XM_017015176 BEND5 79656 XM_017002331; XM_011542141; XM_017002333; NM_001349795; NR_146232; XM_011542142; XR_001737408; NM_001349794; NM_001302082; NM_001349793; NM_024603 ITGB6 3694 NM_001282354; NM_001282353; NM_000888; NM_001282389; NM_001282390; NM_001282355; NM_001282388 AGL 178 NM_000646; XM_005270557; NM_000644; NM_000028; NM_000643; XM_017000501; NM_000642; NM_000645 CAMK2N1 55450 NM_018584 GAL3ST1 9514 XM_017029096; XM_024452304; NM_001318107; NM_001318111; NM_001318109; NM_001318114; XM_011530528; NM_001318105; NM_004861; XM_011530518; XM_011530524; NM_001318106; XM_011530522; XM_017029097; NM_001318108; NM_001318110; NM_001318103; NM_001318113; NM_001318116; XM_017029098; NM_001318104; NM_001318112; NM_001318115 EEF1A2 1917 NM_001958 REN 5972 NM_000537 CALML3 810 NM_005185 DNTT 1791 NM_004088; NM_001017520 PHLDA2 7262 NM_003311 CTH 1491 XM_005270509; NM_001902; NM_153742; XM_017000416; NM_001190463 PRSS16 10279 XM_017010162; XM_017010164; XM_017010165; XM_017010161; XM_017010163; NM_005865 AADAC 13 NM_001086; XM_005247104 ASGR2 433 XM_006721524; XM_011523866; XM_017024651; XM_024450755; NM_080913; XM_024450757; NM_001201352; XM_005256648; XM_011523865; NM_080912; XM_011523863; NM_080914; XM_006721526; XM_011523862; XM_011523864; XM_017024653; NM_001181; XM_017024652; XM_024450756 SDCBP 6386 NM_001007067; NM_001007069; XM_024447231; NM_001330537; NM_001348340; XM_024447229; NM_001007068; NM_001348341; XM_024447230; NM_005625; NM_001007070; NM_001348339 PAX9 5083 NM_001372076; NM_006194 CCL25 6370 NM_001394634; NM_001394635; NM_001394638; NM_005624; NM_148888; NM_001394636; NM_001201359; NM_001394637 PKP1 5317 NM_000299; NM_001005337 TNFRSF4 7293 XM_011542074; NM_003327; XM_017002232; XM_011542077; XM_011542075; XM_011542076; XM_017002231 ACADL 33 NM_001608; XM_005246517; XM_017003955 ARPP21 10777 NM_001267619; NM_001385487; NM_001385490; NM_001385558; NM_001385573; NM_001385582; NR_169635; NR_169644; NR_170706; NR_170707; XM_017005574; XM_017005584; NM_001385485; NM_001385536; NM_001385581; NM_001385589; NM_001385594; XM_011533301; XM_017005580; XM_017005588; NM_001267616; NM_001385495; NM_001385576; NR_169645; XM_017005596; XM_024453320; NM_001385565; NM_001385566; NM_001385590; NM_016300; NR_169632; XM_011533303; XM_017005590; XM_017005598; XM_024453322; NM_001267617; NM_001385484; NM_001385488; NM_001385517; NM_001385585; NM_001385592; NR_169647; XM_011533299; XM_017005607; XM_024453323; NM_001025069; NM_001385489; NM_001385492; NM_001385496; NM_001385567; NM_001385577; NM_001385584; NM_001385587; NM_001385591; NM_001385593; XM_017005591; NM_001267618; NM_001385486; NM_001385491; NM_001385564; NM_001385578; NM_001385595; NM_198399; NR_169633; XM_011533300; XM_011533302; XM_017005575; XM_017005579; XM_017005612; XM_024453324; NM_001025068; NM_001385497; NM_001385556; NM_001385562; NM_001385563; NM_001385574; NM_001385580; NM_001385588; NR_169646; NR_170705 SLC13A2 9058 NM_001145975; NM_001346683; NM_003984; NM_001145976; XM_006722165; XM_011525450; XM_011525453; XM_011525454; NM_001346684; XM_011525452; XM_011525451 FGFR4 2264 NM_213647; NM_022963; NM_002011; NM_001291980; NM_001354984 CD247 919 NM_001378516; NM_198053; XM_011510144; XM_011510145; NM_000734; NM_001378515 RAB23 51715 NM_183227; NM_001278666; NM_001278668; NM_016277; NM_001278667; NR_103822 FBXL6 26233 NM_024555; NM_012162 EFNA2 1943 NM_001405; XM_017026449; XM_017026450 NR4A2 4929 XR_001738751; XM_011511246; XM_017004220; NM_173171; XM_005246621; XM_017004219; NM_173172; NM_173173; XM_006712553; XR_001738752; NM_006186; XR_427087 GHRH 2691 NM_001184731; NM_021081 Germ_Cell_Neoplasm CCNB1 891 NM_031966 POMC 5443 NM_001319205; NM_001035256; NM_001319204; NM_000939 NR4A2 4929 XR_001738751; XM_011511246; XM_017004220; NM_173171; XM_005246621; XM_017004219; NM_173172; NM_173173; XM_006712553; XR_001738752; NM_006186; XR_427087 CLDN6 9074 NM_021195 DBNDD1 79007 NM_001288709; NM_001288708; NM_001371581; NM_001042610; NM_024043 CAP2 10486 NM_001363534; NM_006366; NM_001363533 ESM1 11082 NM_001135604; NM_007036 EPS8L1 54869 NM_133180; NM_139204; XM_011527052; XM_005259020; NM_017729; XM_011527051; XM_011527050 MEP1B 4225 XM_011526013; XM_011526014; NM_005925; NM_001308171 PLIN1 5346 NM_002666; XM_005254934; NM_001145311 ZWINT 11130 XR_428692; NM_007057; NM_001005413; XM_017015605; XM_024447784; NM_032997; NM_001005414 HAMP 57817 NM_021175 EIF1AY 9086 NM_004681; NM_001278612 MISP 126353 NR_135168; XM_011527686; XM_011527685; NM_173481 MMP9 4318 NM_004994 CLEC1B 51266 NM_001099431; XM_017019395; XM_011520685; XM_017019396; XM_011520686; NM_016509; NM_001393342 ALLC 55821 XM_017004495; XM_017004498; NM_018436; XM_017004496; XM_011510369; XM_011510370; XM_017004497; NM_199232 PGR 5241 XM_011542869; NM_001271161; NR_073142; NM_000926; XM_006718858; NM_001202474; NM_001271162; NR_073141; NR_073143 COL9A1 1297 NM_001851; NR_165185; NM_078485; XM_017010246; XM_011535429; XM_017010247; NM_001377289; NM_001377290; NM_001377291 DNM1 1759 NM_001005336; NM_001374269; NM_004408; NM_001288738; NM_001288739; NM_001288737 KERA 11081 NM_007035 PLA2G2A 5320 NM_001161728; NM_000300; NM_001161729; NM_001161727; NM_001395463 AURKB 9212 NM_001313950; NM_001313953; XM_017025309; XM_017025307; XM_017025308; XM_017025311; NM_001313952; NM_004217; NM_001313954; NR_132730; NR_132731; XM_017025310; NM_001284526; XM_011524072; NM_001256834; NM_001313951; NM_001313955 APOBEC3B 9582 NM_004900; NM_001270411 ADAMTS13 11093 NM_139027; NM_139028; XM_017014235; NM_139026; XM_017014233; XR_001746171; NM_139025; XM_017014232; XM_011518176; XM_017014234; XM_011518178; XM_011518179; NR_024514 PTH1R 5745 NM_001184744; XM_017006933; XM_011533968; NM_000316; XM_017006934; XM_011533967; XM_005265344; XM_017006932 PTCH2 8643 NM_001166292; NM_003738 CYP46A1 10858 NM_006668; XM_005267274; XM_011536365; XM_011536364; XM_017020933 VRTN 55237 XM_011536911; NM_018228 PLVAP 83483 NM_031310 PTHLH 5744 NM_198965; NM_198966; XM_011520774; NM_002820; XM_017019675; NM_198964 COL8A1 1295 NM_020351; NM_001850 DAZL 1618 NM_001351; NM_001190811 NANOG 79923 NM_024865; NM_001297698 CXorf36 79742 XM_006724559; NM_176819; NM_024689; XM_005272670 C9 735 NM_001737 FOXH1 8928 NM_003923 MDFI 4188 XM_005249117; XM_011514626; NM_005586; NM_001300805; XM_011514625; NM_001300804; XM_017010867; NM_001300806 KLF9 687 NM_001206 EDIL3 10085 NM_005711; NM_001278642 LRRTM4 80059 NM_001134745; NM_001330370; NM_001282924; NM_024993; NM_001282928; NR_146416 PRND 23627 NM_012409 GDF3 9573 NM_020634 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 PRM1 5619 NM_002761 LIN28A 79727 XM_011542148; NM_024674 DPP4 1803 NR_166823; NM_001379606; NM_001379605; NR_166824; NM_001935; NM_001379604; NR_166825; NR_166822 IBSP 3381 NM_004967 CYP17A1 1586 NM_000102 VENTX 27287 XM_017016073; NM_014468 LEFTY2 7044 NM_003240; NM_001172425; XM_011544266 GCKR 2646 XM_017003797; XM_011532763; XR_001738699; XM_017003796; NM_001486 AKR1C3 8644 NM_003739; NM_016253; NM_001253909; NM_001253908 GATA4 2626 NM_001308093; NM_002052; NM_001308094; NM_001374273; NM_001374274 PLP1 5354 NM_001128834; NM_000533; NM_001305004; NM_199478 ADAM11 4185 XM_005257373; NM_001318933; NM_002390; XM_024450754 PRM2 5620 NM_001286358; NR_104428; NM_002762; NM_001286356; NM_001286359; NM_001286357 MUC1 4582 NM_001204292; NM_001204286; NM_001204291; NM_001204285; NM_001204287; NM_001204288; NM_001204289; NM_001204290; NM_001204295; NM_001204297; NM_001204296; NM_001018016; NM_001018017; NM_001044390; NM_001044391; NM_001044392; NM_001044393; NM_001204293; NM_001204294; NM_002456 HAPLN1 1404 XM_017009052; XM_017009051; NM_001884; XM_017009054; XM_017009053; XM_011543168 DEPDC1 55635 NM_001114120; NM_017779 SLPI 6590 NM_003064 C3orf36 80111 NM_025041; NR_161373 PEG3 5178 NM_001369718; NM_001146184; NM_001369719; NM_001369734; NM_001369739; NR_161475; NM_001369731; NM_001369720; NM_001369724; NM_001369732; NM_001369733; NM_001146187; NM_001369722; NM_001369723; NM_001369726; NM_001369728; NM_001369735; NM_001369736; NM_001369737; NM_001369738; NM_001146185; NM_001369717; NM_001369721; NM_001369725; NM_006210; NM_001369729; NM_001369730; NM_001369727; NR_161476; NM_001146186 MLANA 2315 NM_005511 TREM2 54209 NM_001271821; NM_018965 GDF2 2658 NM_016204 DPPA4 55211 XM_011512954; XM_024453622; NM_001348929; NM_001348928; NM_018189 CDH15 1013 NM_004933 RRM2 6241 NR_161344; NM_001034; NR_164157; NM_001165931 MYL7 58498 XM_011515464; NM_021223; XM_011515465; XM_011515463; XM_017012478; XM_017012479; XM_024446851; XM_005249817 PRR7 80758 NM_001375594; NM_030567; NM_001174102; NM_001174101; NM_001375593 PHC1 1911 XM_017018958; XM_011520600; XM_017018955; XM_017018957; XM_011520599; XM_017018956; XM_011520603; XM_005253334; NM_004426 Neuroendocrine_small_cell CD34 947 NM_001025109; NM_001773 NCAM1 4684 NM_001386289; NM_001386290; NM_001386291; NM_001386292; NM_001076682; NM_000615; NM_001242608; NM_181351; NM_001242607 MOGAT2 80168 XM_024448696; NM_025098; XM_011545267 COL11A1 1301 XM_017000337; XM_017000335; XM_017000336; NR_134980; NM_080629; XM_017000334; NM_001190709; NM_001854; NM_080630 DTL 51514 XM_011509614; NM_001286229; NM_001286230; NM_016448 MYOC 4653 NM_000261 FOXA1 3169 NM_004496; XM_017021246 IBSP 3381 NM_004967 GLP2R 9340 XM_011524077; NM_004246; XM_017025340; XM_005256861; XM_017025339; XM_017025341 C14orf105 55195 XM_006720188; XR_001750402; NM_001283056; XM_006720189; XR_001750401; NM_001283057; NM_001283058; NM_001283059; XM_005267810; NM_018168; XM_005267813; XM_005267806; XM_005267811; XR_001750400; XM_005267814; NM_001283060 ZNF185 7739 XM_005274744; XM_017029823; XM_017029829; NM_001178107; XM_005274735; XM_005274740; XM_005274741; XM_017029825; XM_017029831; NM_001178106; NM_001178113; XM_005274738; XM_005274731; XM_017029822; XM_017029826; XM_017029827; XM_017029832; XM_005274745; XM_017029824; NM_001178108; NM_001178110; XM_011531195; XM_017029828; NM_001178115; NM_007150; NM_001178114; XM_005274730; XM_017029821; XM_011531194; NM_001178109; NM_001395254; XM_005274746; XM_017029830; XM_017029833; NM_001388432; XM_005274742; XM_017029834; XM_017029835 SYN2 6854 XM_006713312; XR_001740240; XM_006713311; XM_006713313; NM_133625; NM_003178; XM_017007087 KRT2 3849 NM_000423 ANGPTL4 51129 NM_016109; NM_139314; XM_005272484; XM_005272485; NR_104213; NM_001039667 GABRG3 2567 XM_017022058; XM_017022060; XM_024449889; NM_033223; XM_011521430; NM_001270873; XM_011521431; XM_017022059 SPP1 6696 NM_001251829; NM_001040060; NM_001251830; NM_000582; NM_001040058 SYT12 91683 XM_011545346; XM_011545347; NM_177963; XM_017018547; NM_001177880; NM_001318775; XM_017018548; XM_006718737; XM_024448766; NM_001318773 DPP6 1804 NM_001364499; NR_157196; NM_001364500; XM_017011812; NM_001290252; NM_001364498; NM_001364501; NM_001039350; NM_001936; NM_130797; NR_157195; NM_001290253; NM_001364502; NM_001364497 DLL3 10683 NM_016941; NM_203486 SFRP5 6425 NM_003015 GABRD 2563 XM_011541194; XM_017000936; NM_000815 CCNB1 891 NM_031966 PRL 5617 XM_011514753; NM_000948; NM_001163558; XM_011514754 RETN 56729 NM_020415; NM_001385725; NM_001385727; NM_001385726; NM_001193374 PPM1H 57460 XM_017019676; XM_011538578; NM_020700; XM_011538579 ESM1 11082 NM_001135604; NM_007036 CELA3B 23436 NM_007352 CHGA 1113 NM_001301690; NM_001275; XM_011536370 GGCT 79017 NM_001199816; NM_001199817; NM_001199815; NM_024051; NR_037669 ADH1B 125 NM_001286650; NM_000668 AOC3 8639 XR_934584; NM_001277732; NM_003734; NR_102422; XM_011525419; XR_001752673; XM_011525420; XM_024451015; NM_001277731 AGL 178 NM_000646; XM_005270557; NM_000644; NM_000028; NM_000643; XM_017000501; NM_000642; NM_000645 CELSR3 1951 NM_001407 CLDN3 1365 NM_001306 STRA6 64220 NM_022369; NM_001199042; XM_011521883; XM_011521885; NM_001142618; XM_017022479; NM_001142617; NM_001142619; NM_001142620; XM_011521884; XR_931877; XM_017022478; XM_017022480; NM_001199040; NM_001199041 ALAS2 212 NM_001037968; NM_001037967; NM_000032; NM_001037969 CST1 1469 NM_001898 CA1 759 NM_001128831; NM_001291967; NM_001164830; NM_001738; NM_001128830; NM_001128829; NM_001291968 AOC1 26 XM_017011946; NM_001091; XM_017011947; NM_001272072; XM_017011944; XM_017011945 LIMS2 55679 XM_006712627; XM_024452983; NM_017980; NM_001256542; XM_017004469; NM_001161403; XM_011511453; XM_024452984; NM_001136037; XM_024452986; XR_922961; NM_001161404; XM_006712628; XM_024452985; XM_005263710 HSF2BP 11077 XM_017028269; XM_017028272; XM_011529446; XM_017028270; XM_017028271; XM_017028267; XM_017028268; XR_937435; XM_011529445; XM_011529443; XM_011529447; NM_007031 CDK4 1019 NM_000075; NM_052984 HBB 3043 NM_000518 HOXC10 3226 NM_017409 KRT1 3848 NM_006121 TTC22 55001 XM_017001582; XM_011541671; NM_001114108; NM_017904 TLN2 83660 XM_017022669; XM_005254713; XM_005254715; XM_006720717; XM_017022667; XM_005254714; XM_005254708; XM_005254710; XR_001751405; NM_001394547; XM_005254712; NM_015059; XM_017022666; XM_024450087; XM_005254711; XM_017022665; XM_017022668 S100A12 6283 NM_005621 KRT24 192666 XM_017024299; NM_019016; XM_006721739; XM_011524460 MET 4233 NM_001324402; NM_001324401; XM_006715990; NM_001127500; XM_011516223; NM_000245; XR_001744772; DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 HOXC11 3227 NM_014212 GUCA2A 2980 NM_033553 PTH1R 5745 NM_001184744; XM_017006933; XM_011533968; NM_000316; XM_017006934; XM_011533967; XM_005265344; XM_017006932 ULBP2 80328 NM_025217; XM_017011321 TGM3 7053 NM_003245 CTRB2 440387 NM_001025200 CKM 1158 NM_001824 ALDOC 230 XM_005257949; NM_005165; XM_011524556 CCL23 6368 NM_005064; XR_429910; NM_145898 MMP11 4320 NM_005940; NR_133013 SYNDIG1 79953 XM_011529349; XM_011529352; XR_937144; NM_001323607; XM_017028064; XM_017028065; XM_017028066; XM_011529350; XM_011529348; XM_011529351; XM_011529356; XM_011529358; XM_017028068; XM_017028069; XM_011529347; XM_017028067; NM_001323606; NM_024893; NR_147606; XM_011529353; XM_011529354 HOXC13 3229 NM_017410 MAGEA3 4102 XM_011531161; XM_005274676; XM_006724818; XM_011531160; NM_005362 INS 3630 NM_001185098; NM_001185097; NM_000207; NM_001291897 NKX6-1 4825 NM_006168 HINT1 3094 NR_134495; NM_005340; NR_073488; NR_024610; NR_134494; NR_024611 GRIN2D 2906 XM_011526872; NM_000836 FCN3 8547 NM_173452; NM_003665 MGLL 11343 XM_017005665; NM_001256585; NM_001388313; NM_001388318; NM_001388317; XM_011512383; NM_001003794; XM_017005663; XM_024453334; NM_001388312; NM_001388315; NM_007283; XM_011512382; NM_001388314; NM_001388316 FMO2 2327 NM_001460; NR_160266; XR_921761; NM_001365900; XR_001737072; NM_001301347 PCSK2 5126 NM_002594; NM_001201529; NM_001201528 MYL2 4633 NM_000432 SIM1 6492 XM_011536072; NM_001374769; NM_005068 EFNA3 1944 NM_004952 MT1M 4499 NM_176870 CST4 1472 NM_001899 P2RY14 9934 XM_011513340; NM_001081455; XM_005247922; NM_014879; XM_017007583; XM_005247923 MMP14 4323 NM_004995 CDH19 28513 XM_011525931.3; XM_017025711.2; XM_011525932.1 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 ETV4 2118 NM_001261437; NM_001261439; NM_001986; NM_001369368; NM_001079675; NM_001261438; XM_024450644; NM_001369366; NM_001369367 SIX1 6495 XM_017021602; NM_005982 ABCA12 26154 XM_011510951; NR_103740; NM_173076; NM_015657 BARX2 8538 XM_011543043; NM_003658; XM_011543044 CRISP2 7180 XM_011514841; XM_011514842; XR_002956303; NM_001142417; NM_001261822; NM_003296; XM_011514843; XR_926302; XM_005249350; XM_005249352; XM_005249349; XM_005249353; XR_002956302; XM_005249351; NM_001142435; XM_005249356; XR_002956301; NM_001142407; XR_002956300; XR_926303; NM_001142408 IGFBP3 3486 NM_000598; NM_001013398 CA7 766 NM_001365337; XM_011523312; NM_001014435; NM_005182 PPEF1 5475 NM_001377996; NM_001377994; NM_001389623; NM_001377986; NM_006240; NM_152224; NM_152226; NM_152225; NM_001378381; NM_001389624; NM_152223; NM_001377993; NM_001378382; XM_017029612; NM_001389621; NM_001377995; NM_001389620 Clear_Cell_Renal_Cell_Carcinoma NKX2-4 644524 NM_033176 LCN2 3934 NM_005564 HGFAC 3083 NM_001297439; NM_001528 TNNI3 7137 NM_000363 NMRK2 27231 NM_001289117; NM_001375468; NM_001375469; NM_170678; NM_001375467; NM_014446; XM_006722725; NR_110316 NKAIN3 286183 XM_017013359; XM_011517511; XM_017013360; XM_017013361; NM_001039769; NR_130764; NR_027378; XM_011517512; NM_173688; NM_001304533 ARHGAP40 343578 NM_001164431 KRT7 3855 XM_017019294; XR_001748700; NM_005556; XM_011538325; XR_001748699 CST4 1472 NM_001899 SFTPC 6440 NM_001317779; NM_001385656; NM_001385658; NM_001385659; NM_001172410; NM_001385654; NM_001385655; NM_001317778; NM_001317780; NM_001385657; NM_001385660; NM_001385653; XM_011544613; NM_001172357; NM_003018 DNTT 1791 NM_004088; NM_001017520 LRRN4 164312 XM_011529183; NM_152611 NPBWR1 2831 NM_005285 CLDN3 1365 NM_001306 CXCL11 6373 NM_001302123; NM_005409 CD36 948 XM_024447002; NM_000072; NM_001289909; NM_001371081; NR_110501; NM_001001548; NM_001127443; XM_005250715; NM_001371074; NM_001001547; NM_001371075; NM_001127444; NM_001371077; NM_001371078; NM_001371079; NM_001371080; XM_024447003; NM_001289908; NM_001289911 B4GALNT1 2583 XM_024448928; XR_002957307; XM_011538147; XM_024448929; NM_001276469; XM_017019141; NM_001276468; XM_005268773; XM_017019140; NM_001478; XM_017019142 HTR1F 3355 NM_001322208; XM_005264751; NM_000866; NM_001322210; NM_001322209; XM_011533664 IFNG 3458 NM_000619 GRIN2A 2903 XM_017023172; NM_001134407; XM_011522461; NM_001134408; NM_000833; XM_011522458; XM_017023173 REN 5972 NM_000537 HILPDA 29923 NM_013332; NM_001098786 EGLN3 112399 NM_001308103; NM_022073 C14orf180 400258 XM_005267638; NM_001286399; NM_001286400; XM_011536764; NM_001008404 CIB4 130106 XM_024452692; NM_001029881; XM_017003329; XM_017003331; XM_011532514; XM_017003330 CTAGE9 643854 NM_001145659 IGFBP1 3484 NM_000596; NM_001013029 GDF6 392255 NM_001001557 APOB 338 NM_000384 PCSK6 5046 NM_001291309; NM_138322; NM_138325; NM_138320; NM_138324; NM_138319; NM_138321; NM_002570; NM_138323 LOX 4015 NM_001317073; NM_001178102; NM_002317 DAZ2 57055 NM_001388495; NM_001389303; NM_001005785; NM_001388494; NM_001005786; NM_001388493; NM_020363 DAZ4 57135 XM_011531509; NM_020420; NM_001388484; NM_001005375; XM_011531510 Papillary_Renal_Cell_Carcinoma FABP7 2173 NM_001319039; NM_001319041; NM_001319042; NM_001446 KLK15 55554 XM_011527088; XR_001753713; NM_001277081; NM_017509; NM_138563; XM_011527085; XM_011527087; XM_011527089; NM_023006; XM_006723265; NM_138564; XM_017026943; NM_001277082; NR_102274 NDUFA4L2 56901 NM_001394961; NM_001394960; NM_020142 KISS1R 84634 NM_032551; XM_017027382 EBF2 64641 NM_022659 FGG 2266 NM_000509; NM_021870 MCHR1 2847 NM_005297 STAP1 26228 NM_001317769; NM_012108; XM_017008018 CP 1356 XM_006713500; XM_006713501; XM_017005735; XM_017005734; XM_006713499; XM_011512435; XR_427361; NM_000096; NR_046371 DAZ1 1617 XM_011531482; NM_004081; XM_011531483; NM_001388496 LOX 4015 NM_001317073; NM_001178102; NM_002317 IGFBP1 3484 NM_000596; NM_001013029 RGS5 8490 NM_003617; NM_001195303; NM_001254748; NM_001254749 REN 5972 NM_000537 FBN3 84467 NM_032447; XM_017027374; XM_017027376; NM_001321431; XM_017027372; XM_017027373; XM_017027378; XM_017027375; XM_017027377; XM_017027379 PTPRN 5798 NM_002846; NM_001199764; NM_001199763 APOB 338 NM_000384 GRIK3 2899 NM_000831 APLN 8862 NM_017413 CA9 768 XR_428428; NM_001216; XR_001746374 CD36 948 XM_024447002; NM_000072; NM_001289909; NM_001371081; NR_110501; NM_001001548; NM_001127443; XM_005250715; NM_001371074; NM_001001547; NM_001371075; NM_001127444; NM_001371077; NM_001371078; NM_001371079; NM_001371080; XM_024447003; NM_001289908; NM_001289911 UBTFL1 642623 NM_001143975 SPARCL1 8404 NM_001291976; NM_004684; NM_001291977; NM_001128310 SLCO1C1 53919 XR_001748769; XR_001748771; NM_001145946; XM_017019486; NM_001145945; XM_011520703; XR_001748768; XR_001748770; XM_005253394; XM_011520711; XM_024449024; XM_017019487; NM_017435; XM_005253396; NM_001145944; XM_024449025; XM_017019489; XM_011520704; XM_017019490 CIB4 130106 XM_024452692; NM_001029881; XM_017003329; XM_017003331; XM_011532514; XM_017003330 TUBA3E 112714 NM_207312 COX4I2 84701 XM_005260580; XM_005260581; NM_032609; XM_005260579 ERP27 121506 NM_152321; NM_001300784 CREB3L3 84699 NM_001271997; NM_032607; NM_001271995; NM_001271996 BAALC 79870 XR_001745601; NM_001024372; NM_001364874; NM_024812 MEOX2 4223 NM_005924 CSPG4 1464 NM_001897 GRIN2A 2903 XM_017023172; NM_001134407; XM_011522461; NM_001134408; NM_000833; XM_011522458; XM_017023173 LHX9 56956 NM_001014434; NM_020204; XM_005245350; XM_011509781; XM_017001849; NM_001370213 GABRQ 55879 NM_018558; XM_011531184 AVPR1A 552 NM_000706 COL25A1 84570 XM_011532334; NM_001256074; XM_011532358; NM_032518; NM_198721; XM_011532333; XM_011532356; XM_017008736; XM_017008737; NR_045756; XM_011532338; XM_017008735; XM_011532335; XM_011532355 ASB5 140458 XM_005262759; XM_011531617; NM_080874; XM_011531616 ADAMTSL1 92949 XM_017015311; NM_052866; XM_011518063; XM_011518067; XM_017015313; NM_001040272; XM_011518064; XM_011518068; NM_139238; XM_017015310; XM_011518070; XM_017015312; XM_017015314; NM_139264 FHL5 9457 NM_001170807; NM_001322466; NM_001322467; NM_020482 DEFB132 400830 NM_207469 CTAGE9 643854 NM_001145659 OPN4 94233 NM_001030015; XM_017016955; XM_017016956; XM_017016957; NM_033282 CXCL11 6373 NM_001302123; NM_005409 ACAN 176 XM_011521313; XM_011521314; NM_001135; NM_001369268; NM_013227 B4GALNT1 2583 XM_024448928; XR_002957307; XM_011538147; XM_024448929; NM_001276469; XM_017019141; NM_001276468; XM_005268773; XM_017019140; NM_001478; XM_017019142 ADGRL4 64123 NM_022159 SMOC1 64093 NM_001034852; NM_022137; XM_005267996; XM_005267995 SLC38A8 146167 NM_001080442; XM_017022946 DNAAF3 352909 NM_001256716; NM_001256714; NM_001256715; NM_001031802; NM_178837 IGFBP6 3489 NM_002178 SLC47A2 146802 NM_001099646; XM_017024221; XM_017024225; XM_017024222; XM_017024224; XM_017024226; XR_001752432; XM_017024223; NR_135624; NM_001256663; NM_152908; NR_135625; XR_001752433 SFN 2810 NM_006142 CPNE4 131034 XM_017005695; NM_130808; XM_017005694; NM_001388327; XM_024453338; XM_011512408; XM_024453339; NM_001388326; NM_153429; XM_017005696; XM_024453340; NM_001289112 CST6 10395 NM_001316668; NM_182643; XM_005273374; NM_001348081; NM_001348083; NM_001348084; NM_001164271; NM_006094; NM_024767; NM_001348082 CLDN3 1365 NM_001306 PIGR 5284 XM_011509629; NM_002644 CPLX2 10814 XM_005265798; XM_005265799; XM_017008964; NM_032282; NM_001008220; NM_006650; XM_011534419 LRRN4 164312 XM_011529183; NM_152611 WFDC5 149708 NM_001395506; NM_145652; XM_011528601; XM_011528602 NPBWR1 2831 NM_005285 PRKCG 5582 NM_002739; NM_001316329 ARHGAP40 343578 NM_001164431 KRT23 25984 NM_001282433; XM_005257200; XM_011524595; NM_015515; NM_173213 HS3ST4 9951 NM_006040 SPAG6 9576 NM_001253855; XM_005252646; XM_005252645; NM_172242; NM_001253854; NM_012443 HGFAC 3083 NM_001297439; NM_001528 CNTN6 27255 NM_001289081; NM_001349352; NM_001349356; XM_017006174; NM_001349361; XM_011533591; NM_001349358; NM_014461; NM_001289080; NM_001349353; NM_001349359; XM_011533590; NM_001349350; NM_001349357; NM_001349354; XR_940415; NM_001349351; NM_001349355; NM_001349360; XM_017006171; XM_017006172; XM_017006177; NM_001349362 LCN2 3934 NM_005564 AKR1B10 57016 XR_927491; XM_011516416; XM_011516417; NM_020299 SCEL 8796 XM_006719884; XM_011535281; XM_011535284; XM_011535285; XM_011535288; XM_011535289; NM_144777; XM_006719882; XM_011535291; XM_017020805; XM_006719885; XM_011535283; XM_011535287; XM_011535290; NM_003843; XM_005266578; NM_001160706; XM_011535282; XM_011535286 NKX2-4 644524 NM_033176 Chromophobe_Renal_Cell_Carcinoma REG1A 5967 NM_002909 PADI3 51702 NM_016233; XM_011541571; XM_017001463; XM_011541572 MUC12 10071 NM_001164462 AVPR1B 553 NM_000707 CTSE 1510 XM_011509245; NM_001910; NM_148964; XM_011509244; NM_001317331 KRT6A 3853 NM_005554 KRT6B 3854 NM_005555 SLC17A2 10246 XM_006714951; XM_017010160; XM_006714949; XM_006714950; NM_001286123; NM_005835; XM_017010159; NM_001286125 HAVCR1 26762 XM_017009339; XM_024446021; XM_024446023; XM_024446020; XM_024446024; NM_001308156; XM_024446019; XM_011534515; NM_001173393; NM_012206; NM_001099414; XM_024446022 KRT6C 286887 NM_173086 TMEM196 256130 NM_001366626; NM_001366628; XM_017011929; NM_001366627; NM_152774; NM_001363562; XM_017011928; NM_001366625 CDH6 1004 NM_004932; NM_001362435; XM_017008910; XM_011513921; XR_001741972 PSORS1C2 170680 NM_014069 LYZL1 84569 XR_428650; XM_017016791; NM_032517; XM_005252627 KRT33B 3884 NM_002279 C4orf51 646603 XM_024454188; XR_002959750; XR_002959751; XR_002959755; XR_002959756; XM_024454189; XR_002959749; XR_002959752; NM_001080531; XM_024454190; XR_002959748; XR_002959746; XR_002959747; XR_002959753; XR_002959754 PSG5 5673 NM_001130014; XM_011527132; NM_002781; XM_017027003 UMODL1 89766 XM_017028508; NM_001199527; XM_017028507; NM_001004416; NM_001199528; NM_173568; XM_011529797 DEFB132 400830 NM_207469 PIP 5304 NM_002652 DBX1 120237 NM_001029865 SLC6A2 6530 XM_011523295; XM_011523297; XR_933403; XM_011523299; XM_011523300; NM_001172502; NM_001043; NM_001172501; XM_006721263; XM_011523298; NM_001172504; XM_011523296 SPSB4 92369 XM_017007509; XR_924215; XR_924216; NM_080862 ATP6V0D2 245972 NM_152565 RGS8 85397 XM_011510089; XM_017002634; NM_001387848; XM_017002631; NM_001387849; NM_001369564; NM_001387847; XM_017002632; NM_001102450; NM_033345; XM_011510090; XM_011510091 FOXI1 2299 XR_941092; NM_012188; NM_144769 CLEC2L 154790 XM_017011770; NM_001353368; NM_001080511 AMTN 401138 NM_001286731; NM_212557 Glioblastoma TCEAL2 140597 NM_080390 RBP1 5947 NM_002899; NM_001130992; NM_001130993; NM_001365940 CBLN1 869 NM_004352 FBXO17 115290 NR_104026; NM_148169; NM_024907 CLEC2B 9976 NM_005127 ATOH8 84913 XM_006712122; XM_011533139; XR_939732; XR_001739003; NM_032827; XR_939733; XR_939731 TSTD1 100131187 NM_001113207; NM_001113205; NM_001113206 SNAP91 9892 XM_017011576; XM_024446600; NM_001376676; NM_001376683; NM_001376689; NM_001376690; NM_001376698; NM_001376700; NM_001376710; NM_001376715; NM_001376739; NR_164846; XM_005248770; XM_006715615; XM_011536276; XM_017011575; XM_017011579; XM_017011580; XM_024446599; NR_026669; NM_001256717; NM_001376677; NM_001376687; NM_001376701; NM_001376706; NM_001376713; NM_001376716; NM_001376723; NM_001376736; XM_017011558; XM_017011564; XM_017011566; XM_017011570; NM_001376675; NM_001256718; NM_001376680; NM_001376688; NM_001376694; NM_001376707; NM_001376708; NM_001376711; NM_001376740; XM_017011567; XM_017011590; NM_001376678; NM_001376691; NM_001376705; NM_001376738; NR_164843; XM_011536266; XM_011536269; XM_011536271; XM_011536275; XM_017011562; XM_017011571; XM_017011574; XM_017011582; XM_017011583; XM_017011584; NM_001242792; NM_001363677; NM_001376686; NM_001376712; NM_001376719; NM_001376721; NM_001376731; NM_001376741; XM_011536273; XM_017011559; XM_017011565; XM_017011581; XM_017011585; XM_017011587; XM_017011589; NM_001242794; NM_001376679; NM_001376695; NM_001376696; NM_001376697; NM_001376702; NM_001376709; NM_001376717; NM_001376728; NR_164844; XM_017011569; XM_017011572; XM_017011573; XM_017011577; XM_017011586; NM_001242793; NM_001376681; NM_001376684; NM_001376685; NM_001376692; NM_001376693; NM_001376699; NM_001376703; NM_001376704; NM_001376714; NM_001376720; NM_001376726; NM_001376734; NM_001376737; NM_001376742; NM_014841; NR_164845; XM_011536265; XM_017011557; XM_017011560; NM_001376682; NM_001376718; NM_001376733; NM_001376735 SNX22 79856 NM_024798; XM_005254677; XM_017022581; NR_073534 NDC80 10403 NM_006101 MEOX2 4223 NM_005924 LUZP2 338645 NM_001252008; XM_017017648; XR_930864; NM_001252010; XM_011520056; XM_017017649; NM_001009909 SUSD5 26032 XM_005265034; XM_017006137; NM_015551 ASF1B 55723 NM_018154 CARD16 114769 NM_001394580; NM_052889; XM_011542583; NM_001017534 SH3GL2 6456 NM_003026; XR_001746364; XM_011518005 KLRC2 3822 NM_002260 AURKA 6790 NM_001323304; NM_001323303; NM_198435; NM_198437; XM_024451974; NM_198433; NM_198434; NM_198436; XM_017028034; XM_017028035; NM_001323305; NM_003600 TNFAIP6 7130 NM_007115 FUT9 10690 XM_011535383; XM_011535385; XM_017010188; NM_006581; XM_017010190 TUBA1C 84790 NM_001303114; NM_032704; NM_001303116; NM_001303117; NM_001303115 HDAC4 9759 XM_011512219; XM_011512225; NM_001378415; XM_011512218; XM_017005394; XM_006712879; XM_011512224; XM_017005395; NM_001378416; NM_006037; XM_011512223; XM_011512227; NM_001378414; XM_011512220; XM_011512222; XM_011512230; XM_024453257; XM_011512217; XM_011512226; NM_001378417; XM_006712877; XM_006712880 OPHN1 4983 XM_006724653; XM_011530961; XM_005262270; XM_017029555; NM_002547 DPP10 57628 XM_017004566; NM_001321908; NM_001321910; NM_001178034; NM_001004360; NM_001321905; NM_001321907; NM_001321909; NM_001321911; NM_001321912; XM_024453023; NM_001321906; NM_020868; NM_001178036; NM_001178037; NM_001321913; NM_001321914 CRTAC1 55118 NM_018058; XM_017016367; XM_005269938; XM_011539917; NM_001206528; XM_017016366 SLC22A18 5002 NM_002555; NM_183233; XM_011520142; NM_001315501; XM_011520141; NM_001315502 SSTR1 6751 NM_001049 HMX1 3166 NM_018942; NM_001306142 GDF15 9518 XM_024451789; NM_004864 NALCN 259232 XM_017020537; XM_011521067; XM_011521069; NM_001350748; NM_052867; NM_001350751; NM_001350749; XM_017020536; XM_024449336; NM_001350750 GABRG1 2565 NM_173536; XM_017007990 PHYHIPL 84457 XM_017016783; XM_017016782; XM_011540275; XM_011540276; NM_032439; NM_001143774 TAGEN2 8407 NM_003564; NM_001277223; NM_001277224 PPM1L 151742 NM_001317911; NM_001317912; NR_134243; XM_011512440; NM_139245 OCIAD2 132299 NM_001014446; NM_152398; NM_001286773; NR_104589; NM_001286774 GABRA3 2556 NM_000808; XM_006724811 MEGF11 84465 NM_001385031; XM_017022673; NM_001385030; NM_001387150; NM_032445; NR_169554; NR_169555; NR_169556; NR_169557; NR_169558; XM_017022675; NM_001385029; XM_017022670; XM_017022674; NM_001387151; XM_017022671; XM_017022672; NM_001385028; NM_001385032; NM_001385033 PLCB1 23236 NM_015192; NM_182734 PDPN 10630 NM_001006625; NM_198389; NM_001385053; NM_001006624; XM_006710295; NM_006474; NM_013317; XM_024451404 TOM1L1 10040 XM_017024002; XR_002957936; NM_001321173; NM_001321175; NM_001321174; XR_243612; NM_001321176; NM_005486; XR_001752397 NTNG2 84628 XM_011519105; XM_011519099; XM_011519094; XM_011519097; XM_011519098; NM_032536; XM_011519096; XM_011519100; XM_011519108; XM_011519112; XM_011519104; XM_011519113; XM_017015213; XM_011519102; XM_011519106; XM_011519107; XM_017015216; XM_011519110; XM_017015212; XM_017015215; XM_006717304; XM_011519103; XM_011519109; XM_017015214 PKIB 5570 XM_011535937; NM_181795; XM_011535930; XM_011535931; XM_011535935; XM_011535936; NM_001270393; NM_032471; XM_011535932; NM_001270395; XM_011535933; NM_001270394; NM_181794 SHISA7 729956 NM_001145176; NM_175908 IL1RAP 3556 NM_001364880; NM_001167930; NM_001167931; NM_002182; NM_134470; NM_001167929; NM_001364879; NR_157353; NM_001167928; NM_001364881; NR_157352; XM_017006348 GRID1 2894 NM_017551; XM_011539720 DNM3 26052 XM_017000982; XM_017000983; XM_017000988; NM_001278252; XM_017000977; XM_017000989; NM_001350206; NM_015569; XM_017000979; XM_017000985; XM_017000991; XR_001737110; NM_001136127; NR_146559; XM_017000976; XM_017000978; XR_001737107; NM_001350204; XM_005245079; XM_017000987; XR_001737111; XM_017000980; XM_017000990; XM_017000992; XM_017000984; XM_017000986; XR_001737108; NM_001350205 REPS2 9185 XM_011545605; XM_024452479; XM_011545604; XM_005274625; XM_011545603; XM_005274626; XM_011545607; XM_024452478; XM_017029955; XM_017029956; NM_001080975; NM_004726; XM_017029958; XR_001755742; XM_011545606; XM_011545609; XM_017029957 ATP6V1G2 534 NM_130463; NM_138282; NM_001204078 DIRAS3 9077 NM_004675 SOX8 30812 NM_014587 FCGBP 8857 NM_003890 TIMP1 7076 NM_003254; XM_017029766 CSDC2 27254 NM_014460 DDIT4L 115265 NM_145244 LGALS3 3958 NM_001357678; NR_003225; NM_002306; NM_001177388 G0S2 50486 NM_015714 POSTN 10631 NM_001135934; NM_001286665; NM_001286666; XM_017020355; NM_001330517; NM_006475; XM_005266232; NM_001286667; NM_001135936; XM_017020356; NM_001135935 DSCAML1 57453 XM_011542917; NM_020693; XM_011542920; NM_001367905; XM_011542918; XM_011542919; XM_011542921; XM_011542924; NM_001367904; XM_011542925 Astrocytoma RBP1 5947 NM_002899; NM_001130992; NM_001130993; NM_001365940 FBXO17 115290 NR_104026; NM_148169; NM_024907 HLF 3131 NM_002126; XM_011524705; XR_002957996; NM_001330375; XM_005257269 CNTN3 5067 XM_017006508; NM_020872; NM_001393376; XM_017006509; XM_011533768 TMEM158 25907 NM_015444 CACNG2 10369 XM_017028531; NM_006078; NM_001379051; NR_166440 IRX2 153572 NM_033267; XR_001742016; XM_024454379; NM_001134222; XM_011513979 MEOX2 4223 NM_005924 LSP1 4046 NM_001242932; NM_001013255; NM_001289005; NM_001013254; NM_002339; NM_001013253 LUZP2 338645 NM_001252008; XM_017017648; XR_930864; NM_001252010; XM_011520056; XM_017017649; NM_001009909 ASF1B 55723 NM_018154 LYZ 4069 NM_000239 VIM 7431 XM_006717500; NM_003380 CUX2 23316 XM_011538069; XM_017019081; XM_017019080; XM_011538063; XM_011538070; NM_001370598; NM_015267 CTSC 1075 NM_001114173; NM_148170; NM_001814 GABBR1 2550 XM_011514455; XM_006715047; XM_024446392; NM_001319053; NM_001470; XM_011514453; XR_001743302; NM_021903; XM_005248982; NM_021904; NM_021905; XR_001743303 PBK 55872 NM_018492; NM_001278945; NM_001363040 TUBA1C 84790 NM_001303114; NM_032704; NM_001303116; NM_001303117; NM_001303115 PYGL 5836 NM_002863; NM_001163940 MARCH4 57574 NM_020814 DPP10 57628 XM_017004566; NM_001321908; NM_001321910; NM_001178034; NM_001004360; NM_001321905; NM_001321907; NM_001321909; NM_001321911; NM_001321912; XM_024453023; NM_001321906; NM_020868; NM_001178036; NM_001178037; NM_001321913; NM_001321914 ACSL6 23305 NM_001205247; NM_001205248; NM_001205250; NM_001205251; NM_015256; NM_001009185 CRTAC1 55118 NM_018058; XM_017016367; XM_005269938; XM_011539917; NM_001206528; XM_017016366 SPRY4 81848 XM_011537685; NM_001293289; NM_001293290; NM_030964; XM_017009910; NM_001127496 RASL10A 10633 XM_011529821; NM_001007279; XM_011529822; XM_011529823; NM_006477 UBE2T 29089 NM_001310326; NM_014176 SSTR1 6751 NM_001049 FAS 355 NR_028033; XM_011539765; XM_011539766; NR_028034; NR_135314; NR_135315; NM_152877; XM_011539764; XR_945732; XR_945733; NM_152873; NM_152876; XM_006717819; NM_001320619; NR_028035; NM_152871; NM_152874; NM_152872; NR_028036; NM_152875; XM_011539767; NM_000043; NR_135313 FAM155A 728215 XM_011521109; NM_001080396 PHYHIPL 84457 XM_017016783; XM_017016782; XM_011540275; XM_011540276; NM_032439; NM_001143774 PPM1L 151742 NM_001317911; NM_001317912; NR_134243; XM_011512440; NM_139245 LRRTM4 80059 NM_001134745; NM_001330370; NM_001282924; NM_024993; NM_001282928; NR_146416 CHGB 1114 NM_001819 GABRA3 2556 NM_000808; XM_006724811 MEGF11 84465 NM_001385031; XM_017022673; NM_001385030; NM_001387150; NM_032445; NR_169554; NR_169555; NR_169556; NR_169557; NR_169558; XM_017022675; NM_001385029; XM_017022670; XM_017022674; NM_001387151; XM_017022671; XM_017022672; NM_001385028; NM_001385032; NM_001385033 PLCB1 23236 NM_015192; NM_182734 STOX1 219736 NM_001130162; NM_001130161; NM_001130160; NM_152709; XM_011539454; NM_001130159 CYTL1 54360 NM_018659; XM_017008299 ABCC8 6833 XM_017018204; XM_017018202; XR_001747945; NM_001351296; NM_001351297; XR_001747946; XM_017018201; XR_002957189; NM_001287174; NR_147094; XM_024448668; NM_001351295; XM_017018199; XM_017018197; NM_000352 PDPN 10630 NM_001006625; NM_198389; NM_001385053; NM_001006624; XM_006710295; NM_006474; NM_013317; XM_024451404 FKBP11 51303 NM_001143782; NM_016594; NM_001143781 GPX7 2882 NM_015696 GRID1 2894 NM_017551; XM_011539720 DNM3 26052 XM_017000982; XM_017000983; XM_017000988; NM_001278252; XM_017000977; XM_017000989; NM_001350206; NM_015569; XM_017000979; XM_017000985; XM_017000991; XR_001737110; NM_001136127; NR_146559; XM_017000976; XM_017000978; XR_001737107; NM_001350204; XM_005245079; XM_017000987; XR_001737111; XM_017000980; XM_017000990; XM_017000992; XM_017000984; XM_017000986; XR_001737108; NM_001350205 CLIC1 1192 NM_001288; NM_001287593; NM_001287594 ATP6V1G2 534 NM_130463; NM_138282; NM_001204078 RIMS2 9699 XM_017014008; XM_017014028; XM_024447342; NM_001100117; NM_001348487; NM_001348496; NM_001348503; XM_005251106; XM_017014014; XM_017014019; XM_017014027; XM_024447344; XM_024447345; NM_001348489; NM_001348491; NM_001348505; NM_001348508; NM_001348509; XM_006716698; XM_017014021; NM_014677; XM_017014010; XM_017014022; XM_024447343; NM_001348499; NM_001395653; NM_001395654; XM_011517398; XM_017014009; XM_017014011; XM_017014016; XM_017014024; NM_001282881; NM_001348490; NM_001348497; NM_001348495; NM_001348498; NR_145710; XM_011517395; XM_017014007; NM_001282882; NM_001348484; NM_001348492; NM_001348494; NM_001348500; NM_001348501; NM_001348502; NM_001348504; XM_005251107; XM_017014012; XM_017014015; XM_017014034; XM_024447347; NM_001348488; NM_001348506; NR_145711; XM_017014006; XM_017014017; XM_017014023; XM_017014036; XM_024447346; NM_001348485; NM_001348486; NM_001348493; NM_001348507; NM_001395652 TJP2 9414 XM_011519206; NM_001369871; NM_001369872; XM_011519208; XM_011519209; NM_001369870; NM_004817; XM_011519207; NM_001369874; NM_001170630; NM_001369875; XM_011519204; NM_001170415; NM_001170416; NM_001170414; NM_001369873; NM_201629 KCNIP2 30819 XM_006717812; NM_173342; XM_005269729; XM_005269730; NM_014591; NM_173197; XM_011539731; NM_173191; NM_173195; XM_017016161; NM_173192; NM_173194; NM_173193 RGS9 8787 NM_001081955; NM_003835; NM_001165933 FCGBP 8857 NM_003890 APOC4- 100533990 NR_037932 APOC2 TIMP1 7076 NM_003254; XM_017029766 NTSR2 23620 NM_012344; XM_005246156; XM_006711877; XM_006711876; XM_017003738 CA12 771 NM_001218; NR_135511; NM_206925; NM_001293642 JPH3 57338 NM_001271604; NR_073379; NM_001271605; NM_020655 FAM57B 83723 XM_017023754; XM_017023751; XM_024450465; XM_024450464; XM_017023752; XM_024450466; XM_017023750; XM_005255613; NM_001318504; NM_001352173; XM_005255614; XM_005255615; NM_031478 DDIT4L 115265 NM_145244 RARRES2 5919 XM_017012491; NM_002889 MDK 4192 NM_001012334; XM_011520116; XM_017017764; NM_001270550; NM_001270551; NM_001012333; NM_001270552; NM_002391; NR_073039 FPR1 2357 NM_002029; NM_001193306 CD58 965 XM_017002869; NM_001779; NM_001144822; NR_026665 POSTN 10631 NM_001135934; NM_001286665; NM_001286666; XM_017020355; NM_001330517; NM_006475; XM_005266232; NM_001286667; NM_001135936; XM_017020356; NM_001135935 DSCAML1 57453 XM_011542917; NM_020693; XM_011542920; NM_001367905; XM_011542918; XM_011542919; XM_011542921; XM_011542924; NM_001367904; XM_011542925 Oligodendroglioma ZNF488 118738 NM_153034; XM_006717617; XM_024447789; XM_017015643; NM_001346932; NM_001346933; NM_001346934; XM_011539244; NM_001346936; NM_001346935 RBP1 5947 NM_002899; NM_001130992; NM_001130993; NM_001365940 WNT7B 7477 XM_011530366; NM_058238 SLC7A14 57709 NM_020949; NM_175917 HLF 3131 NM_002126; XM_011524705; XR_002957996; NM_001330375; XM_005257269 CACNG2 10369 XM_017028531; NM_006078; NM_001379051; NR_166440 SVOP 55530 NM_018711 KCNK3 3777 NM_002246; XM_005264293 SUSD5 26032 XM_005265034; XM_017006137; NM_015551 CA4 762 XM_017025012; XR_001752604; NM_000717; XM_005257639; XR_001752608; NR_137422; XR_001752605; XR_001752607; XR_001752610; XM_011525183; XR_001752606; XR_001752609 VIM 7431 XM_006717500; NM_003380 CUX2 23316 XM_011538069; XM_017019081; XM_017019080; XM_011538063; XM_011538070; NM_001370598; NM_015267 HRH3 11255 NM_007232; XM_005260266; XM_017027623 MYH7 4625 XM_017021340; NM_000257 MYT1 4661 NM_004535 GPR158 57512 NM_020752; XM_017016452; XR_930512 PYGL 5836 NM_002863; NM_001163940 ACSL6 23305 NM_001205247; NM_001205248; NM_001205250; NM_001205251; NM_015256; NM_001009185 CRTAC1 55118 NM_018058; XM_017016367; XM_005269938; XM_011539917; NM_001206528; XM_017016366 SPRY4 81848 XM_011537685; NM_001293289; NM_001293290; NM_030964; XM_017009910; NM_001127496 VSIG4 11326 NM_007268; NM_001184830; NM_001184831; XM_017029251; NM_001100431; NM_001257403 UPP1 7378 XM_011515513; XM_011515512; NM_001287426; NR_109837; XM_005249838; NM_001287428; NM_001287430; XM_011515515; NM_001362774; NM_001287429; NM_181597; XM_011515514; NM_003364 PDZD4 57595 NM_001303513; NM_001303512; NM_001303516; NM_001303515; NM_001303514; NM_032512 FAS 355 NR_028033; XM_011539765; XM_011539766; NR_028034; NR_135314; NR_135315; NM_152877; XM_011539764; XR_945732; XR_945733; NM_152873; NM_152876; XM_006717819; NM_001320619; NR_028035; NM_152871; NM_152874; NM_152872; NR_028036; NM_152875; XM_011539767; NM_000043; NR_135313 FAM155A 728215 XM_011521109; NM_001080396 KCNJ9 3765 NM_004983 LRRTM4 80059 NM_001134745; NM_001330370; NM_001282924; NM_024993; NM_001282928; NR_146416 CHGB 1114 NM_001819 GABRA3 2556 NM_000808; XM_006724811 STOX1 219736 NM_001130162; NM_001130161; NM_001130160; NM_152709; XM_011539454; NM_001130159 BATF3 55509 XR_921869; XR_001737289; XM_017001683; NM_018664 CYTL1 54360 NM_018659; XM_017008299 ABCC8 6833 XM_017018204; XM_017018202; XR_001747945; NM_001351296; NM_001351297; XR_001747946; XM_017018201; XR_002957189; NM_001287174; NR_147094; XM_024448668; NM_001351295; XM_017018199; XM_017018197; NM_000352 PDPN 10630 NM_001006625; NM_198389; NM_001385053; NM_001006624; XM_006710295; NM_006474; NM_013317; XM_024451404 FAM222A 84915 XM_006719654; XM_017020055; NM_032829; XM_024449229 SCRT1 83482 NM_031309; XM_024447291 GPX7 2882 NM_015696 DIRAS3 9077 NM_004675 ATP6V1G2 534 NM_130463; NM_138282; NM_001204078 EIF3CL 728689 NM_001317857; NM_001099661; XM_017023620; XM_017023621; NM_001317856 FCGR2A 2212 NM_001136219; NM_021642; XM_011509287; XM_024454040; XM_017000664; XM_017000665; XM_017000663; XR_001737042; XM_017000666; XM_011509290; XM_011509291; XM_024454041; NM_001375296; NM_001375297 KCNIP2 30819 XM_006717812; NM_173342; XM_005269729; XM_005269730; NM_014591; NM_173197; XM_011539731; NM_173191; NM_173195; XM_017016161; NM_173192; NM_173194; NM_173193 PRLHR 2834 NM_004248 FCGBP 8857 NM_003890 KLHDC8A 55220 NM_001271863; NM_001271865; XM_024448121; NM_018203; NM_001271864 FAM57B 83723 XM_017023754; XM_017023751; XM_024450465; XM_024450464; XM_017023752; XM_024450466; XM_017023750; XM_005255613; NM_001318504; NM_001352173; XM_005255614; XM_005255615; NM_031478 BRINP1 1620 NM_014618 CD58 965 XM_017002869; NM_001779; NM_001144822; NR_026665 RDH5 5959 NM_001199771; NM_002905 GFRA1 2674 XM_011539634; NM_001348098; NM_001382557; NM_005264; NM_001382558; NM_001348099; NM_001382560; NM_001382559; NM_001145453; NM_001348096; NM_145793; NM_001382556; NM_001382561 EPN2 22905 NM_001102664; NM_148921; NM_014964 Basal_Breast_Cancer CDH6 1004 NM_004932; NM_001362435; XM_017008910; XM_011513921; XR_001741972 ESR1 2099 XM_011535545; XM_017010378; XM_017010382; XR_001743223; XR_002956266; NM_001385568; XM_017010381; NM_001122741; NM_001328100; NM_001385570; XM_006715375; XM_017010383; NM_001385572; XM_011535547; XM_011535549; XM_017010377; NM_001385571; XM_017010380; NM_000125; NM_001122740; NM_001122742; NM_001291230; NM_001291241; XM_011535543; XM_017010379; NM_001385569 SULT1C3 442038 NM_001008743; XM_017004155; NM_001320878; XM_017004153; XM_017004154 WNT10A 80326 XM_011511930; XM_011511929; NM_025216 NCAM2 4685 XM_024452081; NM_001352594; XM_011529580; NM_001352592; NM_004540; XM_011529575; NM_001352597; XM_011529576; XM_011529582; NM_001352591; XM_011529581; XM_017028356; NM_001352595; XM_011529585; XM_017028357; NM_001352593; NM_001352596 CTCFL 140690 NM_001269041; NM_001269055; NM_001386993; NR_170377; NM_001269054; NM_080618; NR_072975; NM_001269042; NM_001269044; NM_001269047; NM_001269043; NM_001269045; NM_001269051; NM_001386994; NM_001269040; NM_001269048; NM_001269050; NM_001386997; NM_001269052; NM_001386995; NM_001386996; NM_001269046; NM_001269049 UGT2B11 10720 XM_011531550; XM_017007660; NM_001073 KRT16 3868 NM_005557 TFF3 7033 NM_003226 CCL19 6363 NM_006274 DNALI1 7802 NM_003462 EN1 2019 NM_001426 S100B 6285 NM_006272; XM_017028424 BPI 671 XM_024451972; NM_001725 SERHL2 253190 NM_014509; NR_104301; XR_244363; NR_104300; NM_001284334; XM_024452196; XM_017028739; XM_024452197; XR_001755198 UBXN10 127733 XM_005245742; NM_152376; XM_011540699 SLC44A4 80736 NM_001178045; NM_001178044; NM_025257 ROPN1 54763 NM_001394218; NM_001317775; NR_133919; NR_133916; NR_133917; NM_001394219; NM_001317774; NM_001394217; NM_017578; NR_133918; NR_172091 SPINK8 646424 NM_001080525; XM_017007046; XM_024453712; XR_002959568 CT83 203413 NM_001017978 ACTL8 81569 NM_030812; XM_011542212 MIA 8190 NM_006533; NM_001202553 ERBB4 2066 XM_005246376; XM_017003577; XM_017003578; XM_005246377; NM_001042599; XM_017003581; XM_006712364; XM_017003582; XM_017003579; XM_017003580; NM_005235 GABRP 2568 XM_005265872; NM_001291985; NM_014211; XM_024446012 TMEM246 84302 NM_001303107; NM_001303108; NM_032342; XM_024447701; NM_001371233 C1orf64 149563 NM_178840 SPON1 10418 NM_006108 KRT6B 3854 NM_005555 KRT79 338785 NM_175834 KCNT1 57582 XM_017014932; XM_017014933; NM_020822; XM_017014931; XM_011518877; XM_011518878; XM_011518879; NM_001272003; XM_011518880; XM_011518881; XM_024447617; XM_024447618 SHC4 399694 NM_203349; XM_005254375 HORMAD1 84072 NM_001199829; NM_032132; XM_011510054 LRRC31 79782 XM_011513158; XM_011513159; XM_011513160; NM_001277127; NM_001277128; NM_024727; XM_017007204 NRTN 4902 NM_004558 C1QL4 338761 NM_001008223; XM_011538270 TLX1 3195 NM_001195517; XM_011539744; XM_011539745; NM_005521 CLDN8 9073 NM_199328; NM_012132 MGAM2 93432 NM_001293626; NM_001008748; XM_011516692; XM_011516694; NR_003715; XM_024446997; XM_011516693; XR_927547; NR_003717 ST6GALNAC1 55808 NM_018414; XR_002958047; XM_017024842; XM_017024844; NM_001289107; XM_011524995; XM_011524996; XM_017024843; XR_001752559; NR_110309 GFRA3 2676 NM_001496 MAGEA3 4102 XM_011531161; XM_005274676; XM_006724818; XM_011531160; NM_005362 PRR15 222171 NM_001329997; NM_001329996; NM_175887; XM_011515198; XM_011515199 IGF2 3481 NM_001291862; NM_001291861; NM_000612; NM_001007139; NM_001127598 LY6D 8581 NM_003695 TPSG1 25823 NM_012467; XM_011522447; XM_011522446 TAT 6898 NM_000353 SMOC1 64093 NM_001034852; NM_022137; XM_005267996; XM_005267995 MT1H 4496 NM_005951 REEP6 92840 NM_138393; NM_001329556 FOXA1 3169 NM_004496; XM_017021246 IL12RB2 3595 NR_047584; XM_011541384; XM_005270827; XM_006710617; NM_001374259; XM_011541383; NM_001258215; NM_001258216; XM_017001204; NM_001258214; NM_001319233; XM_005270828; XM_017001203; NM_001559; NR_047583 ART3 419 NM_001377183; XM_017008210; XM_024454058; NM_001377173; NM_001377180; XM_024454052; XM_024454061; XM_024454062; XR_002959732; NM_001130017; NM_001377181; XM_017008208; XR_002959733; NM_001377174; XM_024454051; NM_001377179; XM_024454050; XM_024454053; XM_024454054; XM_024454059; XM_024454063; NM_001377177; NM_001377178; NM_001377182; XM_024454056; NM_001179; NM_001377176; XM_017008206; NM_001130016; NM_001377175; NM_001377184; NM_001377185 MLPH 79083 XM_011511812; XM_006712737; XM_006712740; XM_006712739; NM_024101; NM_001281473; NM_001042467; NM_001281474; NR_104019; XM_017004893; XM_017004894 LOR 4014 NM_000427; XM_024447049 GRIK1 2897 NM_001320618; NM_001320616; XM_005260944; NM_001320630; NM_000830; XR_001754829; NM_001320621; NM_001393425; NM_001393426; NM_001330993; NM_001330994; NM_001393424; NM_175611 FDCSP 260436 NM_152997 PKP1 5317 NM_000299; NM_001005337 C6orf15 29113 NM_014070 AADAC 13 NM_001086; XM_005247104 PGR 5241 XM_011542869; NM_001271161; NR_073142; NM_000926; XM_006718858; NM_001202474; NM_001271162; NR_073141; NR_073143 ORM2 5005 NM_000608 ROPN1B 152015 XM_006713513; NM_001012337; XM_005247138; NM_001308313 TBC1D9 23158 NM_015130 NPAS3 64067 XM_005267991; NM_001394989; XM_011537069; XM_017021582; XM_017021584; XM_017021585; XM_017021587; NM_022123; XM_011537067; XM_011537071; NM_001165893; NM_001394988; NM_173159; XM_017021583; XM_017021586; XM_017021588; XM_005267992; NM_001164749 HMGCS2 3158 NM_001166107; XM_011541313; NM_005518 NPR1 4881 XM_017001374; XM_005245218; NM_000906 ELOVL2 54898 NM_017770; XM_011514717; XM_011514716; XM_017010985 CA12 771 NM_001218; NR_135511; NM_206925; NM_001293642 CT62 196993 NR_168259; NM_001102658; NR_168260 Non_Basal_Breast_Cancer CHODL 140578 XM_017028273; NM_001204174; NM_024944; XM_011529453; NM_001204176; NM_001204175; NM_001204177; XM_011529457; NM_001204178 MSLN 10232 NM_001177355; NM_005823; NM_013404 CST4 1472 NM_001899 CEACAM6 4680 NM_002483; XM_011526990 OVGP1 5016 NM_002557 FOLR1 2348 NM_000802; NM_016729; NM_016730; NM_016725; NM_016724 LRRTM1 347730 NM_178839; XM_017003987; XM_017003986 TTC6 319089 XM_017021257; XM_011537431; XM_017021254; XM_024449560; XM_011537430; XM_011537432; XR_943762; NM_001310135; XM_017021256; NM_001368142; XM_017021255; XR_001750287; NM_001007795 SPRR2A 6700 NM_005988 NCAM2 4685 XM_024452081; NM_001352594; XM_011529580; NM_001352592; NM_004540; XM_011529575; NM_001352597; XM_011529576; XM_011529582; NM_001352591; XM_011529581; XM_017028356; NM_001352595; XM_011529585; XM_017028357; NM_001352593; NM_001352596 WNT10A 80326 XM_011511930; XM_011511929; NM_025216 PKHD1L1 93035 XM_017013970; XM_017013969; XM_011517371; XM_017013971; XM_017013972; XM_017013973; XM_017013974; NM_177531 BCAS1 8537 XM_005260591; XM_017028111; XM_005260595; NM_001366295; XM_005260590; XM_011529090; NM_001366298; XM_005260594; XM_005260589; XM_011529091; NM_001366297; NM_001316361; NM_003657; NM_001323347; NM_001366296 SMYD1 150572 NM_198274; NM_001330364 DACT2 168002 NM_001286350; NM_001286351; XM_011535507; NM_214462; NR_104425 AKR7A3 22977 XM_017000714; NM_012067; XM_011541046; XR_001737055 HPX 3263 NM_000613 S100B 6285 NM_006272; XM_017028424 MAL 4118 NM_022438; NM_002371; NM_022440; NM_022439 D4S234E 27065 NM_001287763; NM_001287764; NM_001040101; NR_167932; NM_001382227; NM_001382228; NR_167933; NM_014392 SLC44A4 80736 NM_001178045; NM_001178044; NM_025257 SPINK8 646424 NM_001080525; XM_017007046; XM_024453712; XR_002959568 THSD4 79875 NM_024817; NM_001286429; XM_017022584; NM_001394532; XM_017022586; XM_011522044; XM_017022585; XM_011522043; XM_017022582; XM_017022583 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 NEK10 152110 XM_006712998; XM_011533415; XM_017005765; XR_001740034; NM_001394966; XM_017005768; NM_001394968; XM_024453374; NM_001031741; NM_001394965; NM_001394967; NM_001394971; XM_006712997; XM_006713002; XM_011533413; XM_011533414; NM_001394970; NM_001394964; NM_001394969; XM_006712999; XM_017005762; XM_017005764; NM_001394963; NM_199347; XM_017005763; XM_017005773; XM_024453373; NM_001304384; XM_006713001; XM_017005774; NM_152534 TFAP2B 7021 XM_017011235; XM_017011233; NM_003221; XM_011514837; XM_017011234 MB 4151 NM_001382810; NM_001382809; NM_203378; NM_001362846; NM_001382812; NM_203377; NM_001382811; NM_005368; NM_001382813 OCA2 4948 XM_017022264; XM_017022257; XM_017022258; XM_017022262; XM_017022255; XM_017022263; XM_011521640; XM_017022256; XM_017022261; XR_001751294; NM_001300984; XM_017022265; NM_000275; XM_017022259; XM_017022260 CCNA1 8900 XM_011535294; XM_011535296; NM_001111047; XM_011535295; NM_001111046; NM_003914; NM_001111045 PIK3C2G 5288 XM_017019472; XM_017019476; XM_017019470; XM_017019473; XR_931307; XM_017019475; NM_001288772; XM_011520696; XM_011520697; XM_017019471; NM_001288774; NM_004570; XM_017019474; XM_017019477; XM_011520700; XM_011520701; XM_017019478; XM_017019479 GABRP 2568 XM_005265872; NM_001291985; NM_014211; XM_024446012 C1orf64 149563 NM_178840 MSMB 4477 NM_138634; NM_002443 PSAT1 29968 NM_021154; NM_058179 CPA2 1358 NM_001869 SLC30A8 169026 XM_024447083; NM_001172813; NM_001172814; NM_001172815; NM_001172811; NM_173851 NRTN 4902 NM_004558 ZG16B 124220 NM_145252 ABCC11 85320 XM_017023802; NM_001370496; NM_032583; XM_017023798; XM_011523397; XM_017023797; XM_017023800; XM_017023803; XM_017023799; XM_017023801; NM_001370497; XM_011523398; NM_145186; XM_024450475; XR_001752012; NM_033151 MGAM2 93432 NM_001293626; NM_001008748; XM_011516692; XM_011516694; NR_003715; XM_024446997; XM_011516693; XR_927547; NR_003717 KCNH1 3756 NM_172362; XM_017001246; NM_002238 CALB2 794 NM_007088; XR_002957842; NM_001740; NR_027910; NM_007087 PGC 5225 NM_002630; NM_001166424 FSIP1 161835 XM_011521307; XM_017021972; XM_011521309; NM_152597; XM_011521305; NM_001324338; XM_011521311; XM_011521306 HIF3A 64344 XM_017027133; XM_017027139; XM_024451649; XR_001753736; XR_935849; NM_022462; XM_017027132; XM_017027142; XM_005259152; XM_017027138; NM_152796; XM_005259156; XM_005259155; XM_017027136; XM_017027137; XR_002958343; XM_005259153; XM_017027135; XM_017027140; NM_152794; XM_017027134; XM_017027141; NM_152795 HMP19 51617 NM_015980 PRR15 222171 NM_001329997; NM_001329996; NM_175887; XM_011515198; XM_011515199 SERTM1 400120 NM_203451 MMP3 4314 NM_002422 POU3F3 5455 NM_006236 PCK1 5105 NM_002591; XM_024451888 CHAD 1101 XM_011524214; NM_001267 SLITRK6 84189 NM_032229 SOX10 6663 NM_006941 TAT 6898 NM_000353 PIP 5304 NM_002652 F2RL2 2151 NM_001256566; NM_004101 MT1H 4496 NM_005951 FOXA1 3169 NM_004496; XM_017021246 KRT15 3866 XM_017024614; XM_011524784; NM_002275 TF 7018 NM_001063; NM_001354703; NM_001354704 FAM196A 642938 XM_017016537; XM_017016538; XM_017016539; XM_005252694; XM_017016540; XM_017016541; XM_017016542; XM_017016543; NM_001039762 MLPH 79083 XM_011511812; XM_006712737; XM_006712740; XM_006712739; NM_024101; NM_001281473; NM_001042467; NM_001281474; NR_104019; XM_017004893; XM_017004894 PRSS33 260429 NM_001385462; NM_001385463; NM_001385464; NM_152891; NR_169625 SCX 642658 XM_006716616; NM_001080514; NM_001008271 WNT6 7475 NM_006522 SIAH3 283514 NM_198849 ROPN1B 152015 XM_006713513; NM_001012337; XM_005247138; NM_001308313 HOXC13 3229 NM_017410 NPR1 4881 XM_017001374; XM_005245218; NM_000906 RASGEF1C 255426 NM_175062; NM_001031799 LEMD1 93273 XM_011510163; XM_011510162; XM_011510165; NM_001199052; XM_011510160; XM_011510161; XM_011510164; NR_037583; NM_001001552; NM_001199050; NM_001199051 PRSS50 29122 NM_013270 Squamous_Cell_Carcinoma_of_the_Head_and_Neck IGFBP6 3489 NM_002178 NLGN4Y 22829 XM_011531429; NM_001365586; XM_017030036; NM_001365591; XM_006724874; XM_011531427; XM_011531428; XM_017030041; NM_001164238; NM_001206850; NR_028319; XM_017030039; NR_046355; NM_014893; XM_011531430; NM_001365588; NM_001365592; NM_001394830; XM_017030040; NM_001365584; NM_001365590; XM_024452490; NM_001365593; NM_001394831 SCGB1A1 7356 NM_003357 FGG 2266 NM_000509; NM_021870 PLIN1 5346 NM_002666; XM_005254934; NM_001145311 AGER 177 XR_001743190; NM_001206940; XM_017010328; NM_001206936; NM_001206954; NM_172197; XR_001743189; NM_001136; NM_001206929; NM_001206932; NM_001206934; NR_038190; NM_001206966 MMP13 4322 NM_002427 MYL1 4632 NM_079422; NM_079420 FCN3 8547 NM_173452; NM_003665 IRX4 50805 NM_016358; NM_001278633; NM_001278632; NM_001278635; NM_001278634 BPIFA1 51297 NM_130852; NM_001243193; NM_016583 PAX1 5075 NM_006192; NM_001257096 ADH1B 125 NM_001286650; NM_000668 C4BPA 722 XM_005273252; NM_000715; XM_005273251 CA4 762 XM_017025012; XR_001752604; NM_000717; XM_005257639; XR_001752608; NR_137422; XR_001752605; XR_001752607; XR_001752610; XM_011525183; XR_001752606; XR_001752609 F2RL2 2151 NM_001256566; NM_004101 HOXA13 3209 NM_000522 PCSK2 5126 NM_002594; NM_001201529; NM_001201528 BMP8A 353500 XM_017001198; XM_006710616; XM_011541381; XM_011541382; XR_946642; XR_946640; XR_946641; NM_181809 TBX4 9496 XM_011525490; XM_011525491; NM_001321120; XM_011525495; NM_018488 PKNOX2 63876 NR_168078; NM_001382330; NM_001382335; NR_168084; NM_001382328; NM_001382329; NM_001382341; NR_168083; NM_022062; NM_001382324; NM_001382326; NM_001382334; NM_001382336; NM_001382337; NM_001382340; NR_168079; NR_168080; NR_168081; NM_001382325; NM_001382323; NM_001382327; NM_001382332; NM_001382338; NM_001382339; NR_168076; NR_168077; NM_001382331; NM_001382333; NR_168082 PGC 5225 NM_002630; NM_001166424 RPE65 6121 XM_017002027; NM_000329 GSTM5 2949 NM_000851; XM_005270785; XM_005270784 MYH7 4625 XM_017021340; NM_000257 ATP1A2 477 NM_000702 KIF18B 146909 XM_011524389; NM_001264573; NM_001265577; XM_011524386; NM_001080443; XM_011524390; XM_011524388; XM_011524385; XM_011524387; XM_011524391 SCARA5 286133 NM_173833 FILIP1 27145 NR_110608; XM_011535756; NM_001289987; NM_001300866; XM_005248713; NM_015687; XM_005248715 DCD 117159 NM_001300854; NM_053283 SLURP1 57152 NM_020427 DLX1 1745 NM_178120; NM_001038493 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 TCF21 6943 NM_003206; NM_198392 EN1 2019 NM_001426 KRT14 3861 NM_000526 RPS4Y1 6192 NM_001008 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 ALDH1A2 8854 NM_001206897; NM_170697; NM_170696; NM_003888 CFTR 1080 NM_000492 AMY1A 276 NM_004038; NM_001008221 NAV3 89795 XM_017020172; NM_001024383; NM_014903; XM_011538944 HPN 3249 NM_002151; NM_182983; XM_017026732; NM_001384133; XM_017026731; NM_001375441 MKRN3 7681 NM_005664 SCN7A 6332 NM_002976; XM_006712680; XM_006712682; XM_011511615; XM_017004667; NR_045628 ACTC1 70 NM_005159 MYOG 4656 NM_002479 HOXB5 3215 NM_002147 PKMYT1 9088 NM_001258451; NM_182687; NM_001258450; XM_011522735; XM_024450490; NM_004203; XM_011522734; XM_011522736 HJURP 55355 XM_011511437; NM_001282962; NM_001282963; NM_018410 HP 3240 NM_001126102; NM_005143; NM_001318138 CTSE 1510 XM_011509245; NM_001910; NM_148964; XM_011509244; NM_001317331 KCNK10 54207 NM_021161; NM_138317; XM_011536840; XM_024449628; NM_138318 DLL3 10683 NM_016941; NM_203486 CYP2B6 1555 NM_000767 SNTN 132203 NM_001080537; NM_001348756 CRNN 49860 NM_016190 HOXB8 3218 NM_024016; XM_005257286; XM_017024564 DDX3Y 8653 NR_136716; NR_136718; NR_136719; NR_136721; NM_001122665; NR_136720; NR_136723; NM_004660; NM_001324195; XR_001756014; NM_001302552; NR_136717; NR_136724; NR_136722 EIF1AY 9086 NM_004681; NM_001278612 IBSP 3381 NM_004967 C7 730 NM_000587 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 AJAP1 55966 XM_011541787; NM_001042478; NM_018836; XM_011541786 ADIPOQ 9370 NM_004797; NM_001177800 Squamous_Cell_Lung_Carcinoma C20orf85 128602 NM_178456 KLK10 5655 XM_006723289; XM_005259061; NM_002776; NM_145888; NM_001077500; XM_017026993; XM_006723287; XM_005259062 ACTC1 70 NM_005159 IGFBP6 3489 NM_002178 ADH1B 125 NM_001286650; NM_000668 B4GALNT4 338707 XM_017017654; XR_001747858; NM_178537 C4BPA 722 XM_005273252; NM_000715; XM_005273251 CENPM 79019 NM_001110215; NM_001304372; NM_024053; XM_011530368; NM_001304371; NM_001002876; NM_001304370; NM_001304373 PRAME 23532 XM_011530034; NM_206954; NM_001318126; NM_001318127; NM_001291715; NM_001291719; NM_001291716; NM_006115; NM_001291717; NM_206953; NM_206956; NM_206955 MYOG 4656 NM_002479 CACNG1 786 NM_000727 HOXB5 3215 NM_002147 FABP4 2167 NM_001442 MMP11 4320 NM_005940; NR_133013 SCGB1A1 7356 NM_003357 RSPO1 284654 XM_006710583; NM_001242909; NM_001242908; NM_001242910; NM_173640; NM_001038633 LRRN4CL 221091 NM_203422 ENDOU 8909 NM_001172439; NM_006025; NM_001172440 MMP12 4321 NM_002426 GSTA1 2938 XM_005249034; NM_001319059; NM_145740 TNXB 7148 NM_001365276; NM_019105; NM_032470 HP 3240 NM_001126102; NM_005143; NM_001318138 KLHL41 10324 NM_006063 NEFL 4747 NM_006158 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 NKX2-1 7080 NM_001079668; NM_003317 CTSE 1510 XM_011509245; NM_001910; NM_148964; XM_011509244; NM_001317331 KCNK10 54207 NM_021161; NM_138317; XM_011536840; XM_024449628; NM_138318 VPREB3 29802 NM_013378 TBX4 9496 XM_011525490; XM_011525491; NM_001321120; XM_011525495; NM_018488 TROAP 10024 XM_011537723; NM_005480; XR_944445; XM_011537724; XR_944446; NM_001100620; XM_006719181; NM_001278324 PKNOX2 63876 NR_168078; NM_001382330; NM_001382335; NR_168084; NM_001382328; NM_001382329; NM_001382341; NR_168083; NM_022062; NM_001382324; NM_001382326; NM_001382334; NM_001382336; NM_001382337; NM_001382340; NR_168079; NR_168080; NR_168081; NM_001382325; NM_001382323; NM_001382327; NM_001382332; NM_001382338; NM_001382339; NR_168076; NR_168077; NM_001382331; NM_001382333; NR_168082 PAK7 57144 XM_017027960; XM_017027964; XM_017027962; XM_017027963; XM_017027965; NM_177990; XM_017027961; NM_020341 CASQ2 845 NM_001232 PGC 5225 NM_002630; NM_001166424 AMY1C 278 NM_001346780; XM_017001058; NM_001008219 COX6A2 1339 NM_005205 MUC7 4589 NM_001145006; NM_152291; NM_001145007 CLEC2L 154790 XM_017011770; NM_001353368; NM_001080511 POU6F2 11281 NM_007252; NM_001370959; NM_001166018 ZNF280B 140883 XR_002958666; NM_080764; XM_011529897; XR_002958668; XR_002958667; NR_130642; NR_130643 CRNN 49860 NM_016190 SNTN 132203 NM_001080537; NM_001348756 GREM2 64388 XM_005273226; XM_011544249; NM_022469 OGN 4969 NM_033014; NM_014057; NM_024416 MYH7 4625 XM_017021340; NM_000257 KIF18B 146909 XM_011524389; NM_001264573; NM_001265577; XM_011524386; NM_001080443; XM_011524390; XM_011524388; XM_011524385; XM_011524387; XM_011524391 PLA2G4F 255189 NM_213600; XR_931785; NR_033151; XR_931786 LGSN 51557 XM_017010931; XM_017010929; XM_011535889; XM_011535892; NM_016571; XM_017010930; NM_001143940 AHSG 197 NM_001354571; NM_001354572; NM_001622; NM_001354573 UBE2C 11065 NM_001281742; NM_001281741; NM_181802; NM_181803; NR_104036; NR_104037; NM_007019; NM_181800; NM_181801; NM_181799 DES 1674 NM_001927; NM_001382708; NM_001382710; NM_001382713; NM_001382709; NM_001382711; NM_001382712 RNF223 401934 NM_001205252 MYL1 4632 NM_079422; NM_079420 C1orf116 79098 XM_011509973; NM_001083924; XM_005273259; XM_006711530; NM_023938 BMP5 653 XM_011514817; NM_001329756; XM_024446524; NM_001329754; NM_021073 SCARA5 286133 NM_173833 FCN3 8547 NM_173452; NM_003665 HPN 3249 NM_002151; NM_182983; XM_017026732; NM_001384133; XM_017026731; NM_001375441 LOR 4014 NM_000427; XM_024447049 LDB3 11155 NM_001171610; NM_001368064; NM_007078; NM_001080115; NM_001080114; NM_001368068; NM_001080116; NM_001171611; NM_001368067; NM_001368063; NM_001368065; NM_001368066 DHRS7C 201140 NM_001220493; NM_001105571 CRISP3 10321 NM_001368123; NM_006061; NM_001190986 LY6D 8581 NM_003695 FOXM1 2305 XM_011520932; XM_011520934; NM_001243088; XM_011520930; XM_011520933; XM_011520935; XR_931507; NM_202003; NM_202002; XM_005253676; XM_011520931; NM_001243089; NM_021953 CNTNAP2 26047 XM_017011950; NM_014141 ANLN 54443 XM_017012355; NM_018685; NM_001284302; XM_006715746; XM_017012354; XM_017012356; NM_001284301; XM_006715747 DCD 117159 NM_001300854; NM_053283 C7 730 NM_000587 THBS4 7060 XR_002956176; XM_017009798; NM_001306214; NM_003248; NM_001306213; XM_017009799; NM_001306212 GPR87 53836 NM_023915 MYOT 9499 XM_017010060; XM_017010061; NM_001300911; NM_001135940; XM_017010062; NM_006790 USP43 124739 XM_011523640; XM_011523642; XM_011523641; XM_017024161; XM_017024160; XM_017024159; XM_011523639; NM_001267576; NM_153210; XM_017024162 EMX1 2016 XM_011532697; NM_001040404; NM_004097; XM_005264203 SLURP1 57152 NM_020427 BPIFA1 51297 NM_130852; NM_001243193; NM_016583 KLK5 25818 NM_001077492; XM_011526702; NM_001077491; XM_011526703; NM_012427 GYLTL1B 120071 XM_011519891; NM_001300721; NM_001300722; XM_011519888; XM_006718141; XM_011519890; XM_006718140; XM_011519893; NM_152312; XM_005252787; XM_011519886; XM_011519889; XM_011519892; XM_017017173 HAND2 9464 NM_021973 MYOC 4653 NM_000261 MCEMP1 199675 NM_174918 DCC 1630 XM_011525843; XM_011525844; XM_017025570; NM_005215; XM_017025568; XM_017025569 LRRC26 389816 NM_001013653 KLK13 26085 NM_015596; NR_145464; NM_001348178; NR_145466; NR_145465; XR_935788; NR_145463; NM_001348177; NR_145467 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 KRT4 3851 NM_002272 COL10A1 1300 XM_011535432; NM_000493; XM_011535433; XM_017010248; XM_006715333 DPP6 1804 NM_001364499; NR_157196; NM_001364500; XM_017011812; NM_001290252; NM_001364498; NM_001364501; NM_001039350; NM_001936; NM_130797; NR_157195; NM_001290253; NM_001364502; NM_001364497 MASP1 5648 XM_011512989; XM_017006869; XM_017006870; XM_017006871; NM_001031849; XM_006713701; XM_011512990; NM_001879; NR_033519; XM_017006872; XM_011512991; NM_139125 SGCG 6445 NM_000231; NM_001378245; NM_001378244; NM_001378246 SCN7A 6332 NM_002976; XM_006712680; XM_006712682; XM_011511615; XM_017004667; NR_045628 FEZF1 389549 NM_001024613; XM_011516202; NM_001160264; XM_005250337 SLCO4C1 353189 XM_011543372; XM_011543370; NM_180991 AJAP1 55966 XM_011541787; NM_001042478; NM_018836; XM_011541786 AMN 81693 XM_024449714; XM_011537203; NM_030943; XM_011537202 SDR16C5 195814 NM_001318049; NM_001318050; NM_138969; XM_011517479 AQP4 361 NM_001317387; NM_001364287; NM_001364286; NM_001317384; XM_011525942; NM_001650; NM_001364289; NM_004028 CPNE7 27132 NM_153636; XM_017023139; XM_011523000; XM_017023138; XM_017023140; XM_017023141; XM_011523001; NM_014427 TCF21 6943 NM_003206; NM_198392 PTGER3 5733 XM_011541810; NM_198718; NM_000957; NM_198712; NM_198713; NM_198720; NM_198714; NM_198719; NM_198717; NM_001126044; NM_198715; NR_028292; XR_946714; NM_198716; NR_028293; NR_028294 Cervical_Squamous_Cell_Carcinoma SALL1 6299 NM_001127892; NM_002968 MEOX2 4223 NM_005924 BCHE 590 NR_137636; NM_000055; NR_137635 SYCP2 10388 XM_011528488; XM_011528487; XM_011528493; XM_017027590; XM_011528490; XM_017027586; XM_017027591; NM_014258; XM_011528489; XM_017027589; XM_017027587; XM_017027588; XM_017027592 KDM5D 8284 XM_005262561; XR_002958832; XR_002958834; XR_002958837; XR_244571; NM_001146705; XM_011531468; XR_001756013; XM_024452495; XM_005262560; XM_024452496; XR_001756009; XR_001756011; XR_002958835; XR_001756010; NM_001146706; XR_002958836; XR_430568; NM_004653; XR_001756012; XR_002958833 OLFM4 10562 NM_006418 SYNGR3 9143 NM_004209 SLC6A15 55117 XM_011538525; NM_018057; NM_001146335; NM_182767 ADAMTS20 80070 XM_011538754; XM_017019979; NM_025003; NM_175851 FA2H 79152 XM_011523319; XM_011523317; NM_024306 PGR 5241 XM_011542869; NM_001271161; NR_073142; NM_000926; XM_006718858; NM_001202474; NM_001271162; NR_073141; NR_073143 FOXL2 668 NM_023067 KRT81 3887 NM_002281 HOXA13 3209 NM_000522 KRT36 8689 NM_003771 KRT83 3889 NM_002282 RPS4Y1 6192 NM_001008 TBX5 6910 NM_181486; NM_080717; NM_000192; XM_017019912; NM_080718 ASF1B 55723 NM_018154 E2F8 79733 NM_001256372; XM_011520367; NM_001256371; NM_024680; XR_930907 CASP14 23581 NM_012114; XM_011527861 MYOCD 93649 XM_005256863; NM_001378306; NM_001146312; NM_153604; NM_001146313; XM_017025342 KIF4A 24137 NM_012310 PDLIM3 27295 NM_001114107; XR_938723; NM_001257963; XR_938724; NM_001257962; NR_047562; NM_014476; XR_001741206 PAGE2B 389860 XM_017029513; XM_011530785; XM_011530786; XM_011530787; NM_001015038 RPE65 6121 XM_017002027; NM_000329 POU6F2 11281 NM_007252; NM_001370959; NM_001166018 CDKN2A 1029 XR_929159; XM_011517676; XM_011517675; NM_001363763; NM_001195132; NM_058195; NM_000077; NM_058196; NM_058197; XM_005251343 HOXB8 3218 NM_024016; XM_005257286; XM_017024564 ALDH1A2 8854 NM_001206897; NM_170697; NM_170696; NM_003888 HTR2B 3357 XM_005246520; NM_000867; XM_006712482; NM_001320758 DDX3Y 8653 NR_136716; NR_136718; NR_136719; NR_136721; NM_001122665; NR_136720; NR_136723; NM_004660; NM_001324195; XR_001756014; NM_001302552; NR_136717; NR_136724; NR_136722 NAV3 89795 XM_017020172; NM_001024383; NM_014903; XM_011538944 BARX1 56033 NM_021570 OR2B6 26212 NM_012367 SEMA3D 223117 XM_011515961; NM_152754; NM_001384901; NM_001384902; NM_001384900; NM_001384903 DYNC1I1 1780 NM_001135556; NM_004411; NM_001278422; NM_001278421; NM_001135557 NAP1L2 4674 NM_021963 MYL1 4632 NM_079422; NM_079420 ANO1 55107 XM_006718602; XM_006718605; XM_011545124; XM_011545129; XM_017017956; XM_006718604; NM_001378095; NM_001378096; XM_011545123; XM_011545127; XM_011545131; NM_001378097; NM_018043; NR_030691; NM_001378092; XM_011545126; NM_001378093; NM_001378094 HOXA11 3207 NM_005523 CDC25C 995 XM_011543764; XM_011543760; XM_011543761; XM_011543763; NM_001364026; NM_001364027; XM_005272145; NM_001287582; NM_001287583; NM_001790; NM_022809; XM_006714739; XM_011543759; XM_011543762; NM_001318098; NM_001364028 SLCO1A2 6579 NM_001386879; NM_001386886; NM_001386908; NM_001386920; NM_001386926; NM_001386939; NM_001386959; NM_001386960; XM_011520819; NM_001386881; NM_001386929; NM_134431; NR_170340; NM_001386878; NM_001386946; NM_001386952; XM_024449138; NM_001386890; NM_001386922; NM_001386938; NM_001386947; NM_001386961; XM_011520821; NM_001386927; NM_001386940; NM_001386948; NM_001386949; NM_001386958; NM_001386880; NM_001386882; NM_001386937; NM_001386951; NM_001386962; NM_001386963; NM_001386887; NM_001386921; NM_001386954; NR_170341; NR_170343; NM_005075; XM_017019849; NM_001386919; NM_001386931; NM_001386953; NM_021094 EIF1AY 9086 NM_004681; NM_001278612 RBFOX3 146713 XM_017024209; XM_017024211; XM_024450595; NM_001385812; NM_001385840; NM_001385844; NM_001385847; XM_011524366; XM_017024208; NM_001385805; NM_001385807; NM_001385843; NM_001385845; NM_001025448; NM_001082575; NM_001385804; NM_001385808; NM_001385813; NM_001385836; NM_001385817; NM_001385819; NM_001385823; NM_001385826; NM_001385827; NM_001385828; NM_001385829; NM_001385831; NM_001385833; NM_001385842; XM_011524360; XM_024450593; XM_024450596; NM_001350453; NM_001385809; NM_001385832; NM_001385834; NM_001385838; NM_001039904; XM_011524367; XM_024450592; NM_001385811; NM_001385824; NM_001385835; NM_001385837; NM_001385846; NM_001350451; NM_001385806; NM_001385810; NM_001385820; NM_001385825; NM_001385830; NM_001385839; NM_001385841; NM_001385814; NM_001385815; NM_001385816; NM_001385818; NM_001385821; NM_001385822 RDM1 201299 NM_001163124; NR_027996; NR_027999; XM_011524509; NM_001163122; NM_001163130; NM_001163121; NM_001163125; NR_027998; NM_001163120; NM_001034836; NM_001330194; NM_145654; NR_027997; NR_028000 SCARA5 286133 NM_173833 KCNS1 3787 XM_017027846; NM_002251; NM_001322799 PIANP 196500 NM_001244014; NM_153685; NM_001244015; XM_011520926 C1orf106 55765 XM_011509754; XM_011509755; NM_001367289; NM_001367290; XM_011509756; NM_001142569; NM_018265 HOXA10 3206 NR_037939; NM_153715; NM_018951 AIM1L 55057 NM_017977; XM_011541672; XM_011541673; XR_001737260; NM_001039775; XR_946681; XM_005245918 LEFTY2 7044 NM_003240; NM_001172425; XM_011544266 IRX5 10265 NM_005853; XM_011522809; NM_001252197 TRDN 10345 NM_001251987; NM_001256020; NM_001256021; NM_006073; NM_001256022 CNTNAP2 26047 XM_017011950; NM_014141 FOXA1 3169 NM_004496; XM_017021246 ADGRD1 283383 NM_198827; XM_005253566; XM_011538204; XM_011538208; XM_011538212; NM_001330497; XM_011538205; XM_011538206; XM_011538207; XM_011538209; XM_011538210; XM_011538211 PENK 5179 NM_006211; NM_001135690 AKR1C2 1646 NM_001354; NM_001321027; NM_001135241; NM_205845; NM_001393392 MKRN3 7681 NM_005664 NMU 10874 NM_001292046; XM_011534368; XM_011534367; NM_001292045; NM_006681; NR_120489 DIAPH3 81624 XM_011535258; XM_006719876; XM_024449422; NM_001258367; NM_001258370; XR_941672; XM_011535265; XR_002957479; XR_002957480; NM_001258366; XM_017020789; XR_002957478; NM_001042517; NM_001258368; XM_011535263; XR_001749694; XR_002957477; NM_001258369; NM_030932 MUC2 4583 NM_002457 ZIC5 85416 NM_033132; NR_146224; NR_146225 MYLPF 29895 NM_001324458; NM_013292; NM_001324459 POLQ 10721 NM_199420; NM_006596 SYNDIG1 79953 XM_011529349; XM_011529352; XR_937144; NM_001323607; XM_017028064; XM_017028065; XM_017028066; XM_011529350; XM_011529348; XM_011529351; XM_011529356; XM_011529358; XM_017028068; XM_017028069; XM_011529347; XM_017028067; NM_001323606; NM_024893; NR_147606; XM_011529353; XM_011529354 SMC1B 27127 NM_148674; XM_011530145; XR_244368; XM_011530144; NM_001291501 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 EPHA7 2045 NM_001288630; NM_001376467; NM_001288629; XM_017010366; NM_001376466; NM_001376471; NM_004440; XR_001743218; NM_001376465; NM_001376470; NR_164810; NM_001376468; NM_001376469 TCF23 150921 NM_175769; XM_005264159 Colorectal_Adenocarcinoma EFHC1 114327 NR_033327; NM_001172420; NM_018100 KCNN3 3782 NM_001204087; NM_001365837; NM_001365838; NM_170782; NM_002249 USP49 25862 NM_001286554; NM_018561; NM_001384542 ACTL6B 51412 NR_134539; NM_016188 RBM38 55544 NM_017495; NM_001291780; XM_011528885; XM_005260446; NM_183425 CNNM1 26507 NM_001345888; XM_011539631; XR_002956974; NM_020348; NM_001345887; NM_001345889; NR_144311; XR_945667 DRAP1 10589 NM_006442 CWF19L1 55280 NM_001303406; NM_018294; NM_001303407; NM_001303404; NM_001303405 ADAM12 8038 XM_017016705; NM_001288973; NM_001288974; NM_001288975; XM_017016706; NM_003474; NM_021641; XM_024448210 TSPAN6 7105 XM_011531018; NM_001278741; NM_001278743; NM_001278740; NM_001278742; NM_003270 TAF6L 10629 NM_006473; XM_017017100; XM_005273714 RHBDF1 64285 XM_017023556; XM_017023557; XM_017023558; NM_022450; XM_005255494; XM_005255498; XM_006720921 ZNF135 7694 XM_017027242; NM_001289401; NM_007134; NM_001164530; XM_017027241; XM_006723362; XM_017027240; XM_005259211; NM_001164527; XM_006723363; NM_003436; NM_001164529; NM_001289402 HOXD12 3238 NM_021193 FABP1 2168 NM_001443 PFN2 5217 NM_053024; NM_002628 GAST 2520 NM_000805 PPM1G 5496 NM_177983 ALDH8A1 64577 NM_001193480; NM_022568; NM_170771 NRSN2 80023 XM_017028074; XM_017028076; NM_001323685; XM_011529360; NM_001323679; NM_001323684; NM_024958; NM_001323680; NR_136649; XM_017028075; XM_011529363; XM_006723630; NM_001323682; NM_001323683; XM_017028073; NM_001323681; XM_011529362 DRD4 1815 NM_000797 GKN1 56287 NM_019617 PLA2G12A 81579 NM_030821 VWF 7450 NM_000552 A4GNT 51146 XM_017006543; NM_016161; XM_017006544 ANGEL2 90806 XM_005273345; XR_001737529; XM_005273344; XM_017002776; XR_001737527; NM_001300753; NM_001300757; NM_144567; XM_005273346; XM_017002778; XR_001737530; XR_001737531; XR_001737532; XM_005273347; XR_001737528; XR_247045; XM_017002774; XM_017002777; NR_125333; NM_001300758; NM_001300755; XM_017002775 PTPRCAP 5790 NM_005608 MAGEA10 4109 NM_001251828; NM_021048; NM_001011543 RGS12 6002 XM_017008534; XM_017008531; NM_001394162; NM_002926; NM_198227; NM_198229; NM_198432; NM_198587; NM_001394158; NM_001394159; XM_017008529; XR_924987; NM_001394156; NM_001394163; XM_011513543; XR_002959745; NM_001394154; NM_001394161; NM_198230; XR_427479; NM_001394157; NM_198430; NM_001394155 SRC 6714 XM_017028025; XM_017028026; XM_017028024; XM_011529013; NM_198291; XM_017028027; NM_005417 SLC5A3 6526 NM_006933 HSPB7 27129 NM_001349685; NM_001349688; NM_001349686; NM_001349683; NM_001349682; NM_001349689; NM_001349687; NM_014424 ZC3H3 23144 XM_006716536; XM_017013248; XM_011516944; XM_017013249; XR_928313; XM_011516943; NM_015117 TSSC4 10078 XM_011519830; NM_005706; NM_001297659; XM_006718118; NM_001297661; NM_001297660; NM_001297658 ADAM15 8751 NM_003815; NM_207191; NR_048577; NR_048578; NM_207197; NM_001261464; NM_207196; NM_207195; NR_048579; NM_001261466; NM_001261465; NM_207194 CTF1 1489 XM_011545759; NM_001330; XM_011545760; NR_165660; NM_001142544 TMEM120B 144404 XM_024448851; XM_024448852; NM_001080825 CA12 771 NM_001218; NR_135511; NM_206925; NM_001293642 DBN1 1627 NM_001393631; XM_017009139; NM_004395; XM_011534447; NM_080881; XM_017009140; NM_001363541; NM_001364151; NM_001364152; NM_001393630 CXCL5 6374 NM_002994 CSPG4 1464 NM_001897 FAHD2B 151313 XM_011510746; XM_011510747; XM_024452730; XM_024452731; XR_001738649; XR_002959246; XM_017003471; NM_001320849; XM_011510748; XM_011510745; XM_011510750; XM_017003470; XM_017003472; NM_001320848; NM_199336 KIR3DL2 3812 XM_017026784; XM_011526940; NM_006737; NM_001242867 IGLL1 3543 NM_001369906; NM_020070; NM_152855 CEP 5199 XM_017029575; NM_001145252; NM_002621 IL11 3589 NM_000641; NM_001267718 VEGFB 7423 NM_003377; NM_001243733 PGA5 5222 NM_014224 AR 367 NM_001348064; NM_001011645; NM_001348061; NM_001348063; NM_000044 GGA2 23062 XM_024450200; XM_017023075; NM_015044; NM_138640 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 MYH11 4629 XM_017023250; NM_002474; NM_022844; NM_001040113; NM_001040114; XM_011522502 CETP 1071 XM_006721124; NM_000078; NM_001286085 LRFN3 79414 NM_024509 CPSF4 10898 XM_011515757; XM_017011701; XM_017011702; XM_011515755; NM_001318161; NM_001318160; NM_006693; NM_001081559; NM_001318162; XM_011515756; XM_017011700; XM_017011703 GSDMD 79792 NM_024736; XM_011517301; NM_001166237 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 SATB2 23314 NM_015265; NM_001172517; XM_024452767; XM_024452768; NM_001172509; NR_134967; XM_005246396; XM_011510840; XM_017003656 PRLR 5618 XM_011514068; NM_001204315; XM_017009645; NM_001204318; XM_024446132; NM_001204317; NR_037910; NM_000949; NM_001204316; XM_006714484; XM_011514069; NM_001204314; XM_024446131 HOXA7 3204 NM_006896 KLHL11 55175 NM_018143; XR_001752552 TJAP1 93643 XM_006715254; XM_011514995; NM_001146017; NM_001146018; NM_001350570; NM_001394543; XM_006715257; XM_017011493; XR_926337; NM_001350565; NM_001350568; NM_001394542; NM_001394544; XM_006715250; XM_006715261; XM_006715268; XM_024446587; NM_001350562; XM_017011492; NM_001146020; NM_001350561; NM_001394538; NM_001394541; XM_017011489; XM_024446584; NM_001350566; NM_001350569; NM_080604; XM_006715262; XM_006715263; XM_006715266; XM_024446586; NM_001146016; NM_001350563; NM_001350564; NM_001394539; NM_001394545; XM_006715269; XM_011514996; XM_024446585; NM_001350567; XM_006715251; XM_006715265; XM_006715267; NM_001146019; NM_001394540; NR_146793 L1TD1 54596 NM_001164835; NM_019079 PTPRD 5789 XM_006716835; XM_017014958; XM_017014963; XM_017014968; XM_017014976; XM_017014987; XM_017014988; XM_017014990; NM_001040712; NM_001377947; NM_130391; XM_006716827; XM_006716832; XM_017014970; XM_017014971; XM_017014983; XM_017014985; XM_017014989; NM_001378058; XM_017014960; XM_017014965; XM_017014967; XM_017014979; NM_001377958; XM_017014964; XM_017014974; XM_017014977; XM_017014978; XM_017014986; NM_001377946; NM_002839; NM_130392; XM_006716834; XM_006716837; XM_017014959; XM_017014966; XM_017014984; XM_017014993; XM_017014995; NM_130393; XM_006716833; XM_017014972; XM_017014980; XM_017014981; XM_017014991; XM_024447625; XM_024447627; XM_011517992; XM_017014961; XM_017014969; XM_017014982; XM_017014994; XM_017014992; NM_001171025; XM_006716817; XM_006716823; XM_006716825; XM_017014973; XM_017014975 DAGLA 747 XM_017018239; XM_017018238; NM_006133; XM_017018240 CSF1 1435 NM_000757; NM_172210; XM_017000369; NM_172211; NM_172212 C1orf61 10485 NM_001320454; NR_135260; NR_168070; NR_168072; NR_135267; NR_168071; NR_168073; NM_001320455; NR_135265; NR_135264; NR_135266; NM_001320453; NM_006365; NR_135268; NR_135261; NR_135262; NR_135263 FOXRED2 80020 NM_001102371; NM_024955; NM_001363041; NM_001363042 HSD17B6 8630 XM_024449251; XM_011538927; XM_005269208; XM_011538925; XM_011538926; XM_024449250; XM_005269207; NM_003725; XM_005269209; XM_006719672; XM_024449249 FAIM2 23017 XM_005268730; NM_012306 SORBS1 10580 XM_017015501; XM_017015503; XM_017015510; XM_017015511; XM_017015512; XM_017015539; NM_001034957; NM_001290296; NM_001290297; NM_001290298; NM_001377208; NM_001377209; NM_001384448; NM_001384453; NM_001384456; NM_001384461; XM_006717589; XM_011539155; XM_017015500; XM_017015505; XM_017015509; XM_024447770; NM_001290294; NM_001384450; NM_001384460; NM_015385; NM_024991; XM_011539150; XM_017015506; XM_017015536; XM_024447769; NM_001377206; NM_001384452; NM_001384459; NM_001384463; XM_011539167; XM_017015514; XM_017015515; NM_001290295; NM_001377200; NM_001377207; NM_001384455; NM_001384464; XM_017015504; NM_001034954; NM_001034955; NM_001377201; NM_001384447; NM_001384449; NM_001384457; NM_001384458; NM_006434; XM_011539140; XM_017015502; XM_017015513; XM_017015523; XM_017015525; XM_017015537; XM_017015540; NM_001034956; NM_001377198; NM_001377205; NM_001384462; XM_017015507; XM_017015508; XM_017015517; XM_017015530; XM_017015532; XM_017015533; NM_001377199; NM_001377203; NM_001377204; NM_001384451; NM_001384454; NM_001384465; NM_001377197; NM_001377202 ERF 2077 XM_017026469; NM_001308402; NM_001312656; NM_006494; XM_017026468; NM_001301035 KIAA0907 22889 NM_014949 CD207 50489 XM_011532876; XM_011532875; XM_011532874; NM_015717 SF3A2 8175 NM_007165 AQP5 362 NM_001651; XM_005268838 GABRE 2564 XM_024452360; NM_021990; NM_021984; XM_011531140; XM_017029388; XM_017029389; NM_004961; NM_021987; XM_017029387 RAB40AL 282808 NM_001031834 F7 2155 XM_011537476; XM_011537475; NM_001267554; XM_011537474; NR_051961; XM_006719963; NM_019616; NM_000131 ZNF467 168544 NM_001329856; XM_005249959; XM_005249960; XM_017011799; NM_207336; XM_005249961; XM_011515858; XM_006715864; XM_011515857 HTR2A 3356 NM_001378924; NM_000621; NM_001165947 MAPRE3 22924 XM_011532700; NM_001303050; XM_006711967; XM_017003597; NM_012326 LY6G5C 80741 NM_025262; NM_001002849; NM_001002848 DAZ4 57135 XM_011531509; NM_020420; NM_001388484; NM_001005375; XM_011531510 MTTP 4547 NM_001300785; NM_001386140; NM_000253 CD7 924 XM_011523608; XM_017025316; NM_006137; XR_001752681; XR_001752680 ISG20 3669 NM_002201; NM_001303234; NM_001303236; XM_005254899; XM_006720488; XM_017022148; NM_001303235; NM_001303237; XM_011521521; NR_130134; XM_017022147; NM_001303233 ZSCAN2 54993 XM_024449978; XM_017022393; XM_024449975; NM_017894; NM_181877; XM_024449977; XM_024449976; NM_001007072 CCNL2 81669 XM_024450050; NM_001350499; XR_001737454; XR_946769; NM_001350497; NM_001350500; NR_146722; NM_001320153; NM_001320155; NM_030937; XM_017002420; XR_001737453; XR_002957676; XR_002957678; XR_002957684; NM_001350498; NM_001144867; XR_001737452; XR_001737455; NM_001039577; NR_135154; XM_024450049; XR_001737450; XR_426630; NR_146723; XM_011542216; XR_002957683; NM_001144868 MMP23B 8510 XM_017002617; XR_002957848; XM_017002615; NM_006983 GPA33 10223 XM_017000005; NM_005814 ITPKA 3706 XM_011521522; NM_002220 GPR162 27239 NM_014449; NM_019858 PGA3 643834 NM_001079807 RNF25 64320 XM_017004695; NM_022453 EPN1 29924 NM_001130072; NM_001321263; NM_013333; NM_001130071 PIK3C2G 5288 XM_017019472; XM_017019476; XM_017019470; XM_017019473; XR_931307; XM_017019475; NM_001288772; XM_011520696; XM_011520697; XM_017019471; NM_001288774; NM_004570; XM_017019474; XM_017019477; XM_011520700; XM_011520701; XM_017019478; XM_017019479 CLCN4 1183 NM_001256944; NM_001830 FLOT2 2319 XM_017024394; XM_024450667; XM_017024396; NM_004475; XM_017024395; XM_024450666; NM_001330170; XM_005257953 CACNA1H 8912 XM_006720965; XM_017023820; XM_006720963; XM_006720967; XM_011522724; XR_002957850; XM_005255652; XM_017023821; XM_011522727; XM_017023819; NM_021098; XM_006720968; XM_006720964; NM_001005407 ANXA10 11199 XM_011531571; NM_007193 NOTCH2NL 388677 NM_001395232; NM_001364006; NM_203458; NM_001395231 ADRA1D 146 NM_000678 SLC2A6 11182 XR_001746173; XM_011518189; XM_017014238; NM_001145099; XM_017014237; XR_001746175; XR_001746172; XM_017014236; XR_001746174; NM_017585 SIPA1 6494 XR_247210; NM_153253; XM_005274189; NM_006747 TMEM160 54958 NM_017854 PRDM16 63976 NM_199454; NM_022114 GTPBP6 8225 XM_011546184; XM_011545637; NM_012227; XM_006724447; XM_006724868 TP53I11 9537 NM_001258321; XM_011520478; XM_017018580; NM_001076787; NM_001258323; NM_001318387; NM_001318388; XM_017018581; XM_024448777; NM_001258320; NM_001258324; NM_001318390; NM_006034; NR_134612; XM_011520476; XM_011520475; NM_001318385; NM_001318386; NM_001318389; XM_005253227; XM_011520477; NM_001258322; XM_005253229; NM_001318384 PRRX2 51450 XM_017014803; NM_016307 ADAMTSL4 54507 XM_011509650; XR_001737242; XM_011509648; NM_001378596; XM_011509645; XM_011509652; NM_001288607; XM_011509651; NM_019032; XM_011509649; XM_017001506; XM_011509644; XM_017001507; NM_001288608; XR_921844; NM_025008 PALM 5064 XM_005259565; NM_002579; XM_005259566; XM_017026850; NM_001040134 RNF31 55072 NM_017999; NM_001310332 CLPTM1 1209 NM_001294; NM_001282175; NM_001199468; NM_001282176 CDC14A 8556 NM_033313; NM_001319212; NM_033312; NM_001319211; NM_001319210; NM_003672 NEBL 10529 XM_005252343; NM_001173484; NM_001377323; NM_001377327; XM_011519291; XR_001746996; XR_242691; NM_001377325; NM_001377324; NM_001377326; NM_213569; NM_001010896; NM_001377328; XM_005252344; NM_001377322; NM_001177483; XR_001746995; XM_005252342; XM_017015468; NM_006393; NM_016365 AQP8 343 NM_001169; XM_011545822; XM_011545823 NOL6 65083 NM_022917; NM_130793; XM_017015044; NM_139235 LMF2 91289 NM_001363816; XR_001755368; XR_938349; NM_033200; XM_017029077; XM_006724427; XM_006724426 FBP2 8789 NM_003837 GTPBP2 54676 XM_017010976; XM_024446478; XM_024446475; NM_001286216; XM_024446477; XM_024446476; NM_019096 GNL3L 54552 NM_001184819; NM_019067 FBLN1 2192 NM_006485; NM_006486; NM_001996; NM_006487 DDA1 79016 NM_024050; XM_024451701 ELOVL4 6785 NM_022726 ITGA10 8515 XM_017002623; XR_001737503; XM_017002626; XM_017002628; NM_001303041; NM_001303040; XR_001737502; XM_017002622; XM_017002625; NM_003637; XR_001737501; XR_001737504; XM_005277436; XM_017002624; XM_011510083; XM_011510084; XM_017002627 HOXB9 3219 NM_024017 PAX8 7849 NM_013992; NM_013953; NM_013952; NM_003466; NM_013951 GPR137 56834 XM_017018016; NM_001378083; XR_002957154; NM_001378078; NM_001378081; NM_001378087; XM_011545168; XM_005274100; NM_001170881; NM_001378076; NM_001378079; NM_001378085; NM_001378088; NM_001378089; NM_020155; XM_005274102; NM_001170880; NM_001378077; NM_001378082; NR_165394; NR_165396; XM_024448611; NM_001378086; NR_165397; XM_005274104; XM_011545169; NM_001177358; NM_001170726; NM_001378080; NM_001378084; NR_165395 APBB3 10307 NM_133174; NM_133172; NM_133173; NM_133176; NM_133175; NM_006051 SCGB2A1 4246 NM_002407 MAP4K2 5871 XR_002957155; XM_017018093; XM_024448634; XM_017018095; XM_024448630; NM_001307990; XM_024448629; NM_004579; XM_024448631; XM_024448633; XM_011545204 ZBTB10 65986 NM_001277145; NM_023929; NM_001105539 CLCA1 1179 NM_001285 GSTM1 2944 XM_005270782; NM_146421; NM_000561 CLDN5 7122 NM_001363066; NM_001363067; NM_001130861; NM_003277 MAPK3 5595 XR_243293; NM_001109891; NM_001040056; NM_002746 ZNF428 126299 NM_182498 LYL1 4066 NM_005583 GGT5 2687 XM_017028769; NM_001302464; XM_011530137; XM_017028768; NM_001099781; XM_011530134; XM_011530133; XM_011530135; NM_001302465; XM_005261557; XM_011530136; NM_001099782; NM_004121; XM_005261558 FAM124B 79843 NM_001122779; NM_024785 MTG1 92170 NM_138384 ALPL 249 NM_001177520; NM_001369803; NM_001127501; NM_001369804; NM_001369805; XM_017000903; NM_000478 SLC26A3 1811 NM_000111 TMEM127 55654 NM_001193304; XM_017004452; NM_017849; NM_032218; XM_017004450 EPOR 2057 NR_033663; NM_000121 FBXO17 115290 NR_104026; NM_148169; NM_024907 GALNT14 79623 NM_001253827; XR_001738942; XR_001738941; NM_001329095; XM_017004907; NM_001253826; XR_001738943; XM_017004906; NM_001329097; NM_001329096; NM_024572 RAB11B 9230 NM_004218 CCDC106 29903 NM_001370468; NM_001370467; NM_001370469; NM_001370470; NM_013301; NM_001370471 PCCA 5095 XM_017020609; XM_017020613; XM_017020616; NM_001178004; NR_148030; XM_017020611; XR_001749567; XR_001749568; XR_001749569; NM_001352606; NM_001352610; NM_001352611; NM_001352605; NR_148028; XM_017020615; NM_001352607; NM_001352609; XM_017020607; XR_001749574; XR_931615; NR_148029; XM_011521093; XM_017020605; NM_001352608; NM_001352612; XM_017020606; XR_001749577; NR_148027; XM_017020612; XR_001749576; NM_000282; NM_001127692; NR_148031 GJC1 10052 XM_024450525; XM_005256920; NM_005497; XM_024450526; XM_024450527; XR_934346; NM_001080383 TMEM158 25907 NM_015444 PGC 5225 NM_002630; NM_001166424 IFNA8 3445 NM_002170 HSPB6 126393 NM_144617 CLDN18 51208 NM_001002026; NM_016369 GATA4 2626 NM_001308093; NM_002052; NM_001308094; NM_001374273; NM_001374274 EPB41L2 2037 XM_017010353; XR_001743213; XR_001743215; NM_001350314; XM_011535527; XM_017010352; NM_001135555; NM_001350302; XM_011535525; XM_017010351; XM_017010356; NM_001350305; NM_001350309; NR_146620; XM_017010364; XR_001743216; XR_001743217; NM_001199389; NM_001350301; NM_001350303; NM_001350308; NM_001350312; XM_011535524; NM_001135554; NM_001252660; NM_001350307; NM_001350315; NM_001199388; NM_001350310; NM_001350311; NM_001431; NM_001350306; NM_001350320; XM_011535528; XM_017010350; XM_024446349; NM_001350299; NM_001350304; NM_001350313 TNNT2 7139 XM_011509943; NM_001001430; XM_011509946; XM_017002217; XM_011509941; XM_024449450; XM_024449455; NM_001001432; XM_006711508; XM_011509939; XM_017002216; XM_006711509; XM_011509942; NM_000364; NM_001276346; NM_001276347; XM_011509944; NM_001001431; XM_011509938; XM_011509940; XM_024449454; NM_001276345 ZNF557 79230 NM_024341; NM_001044387; NM_001044388 CDR2L 30850 NM_014603; XM_006721852 LRRC37A2 474170 XM_011524841; XM_011524849; XM_011524850; XM_011524844; XM_011524842; XM_024450774; XM_024450773; NM_001006607; XM_011524846; XM_024450775; NM_001385803; XM_011524843; XM_011524848 ZNF771 51333 NM_016643; NM_001142305 SERPIND1 3053 NM_000185 PAOX 196743 NM_152911; NM_207125; NM_207126; NR_109764; NM_207129; NM_207127; NR_109763; NR_109765; NM_207128; NR_109766 PITX1 5307 NM_002653 RET 5979 NM_020975; NM_001355216; NM_020630; NM_020629; NM_000323 CNGA3 1261 XM_006712243; NM_001298; NM_001079878; XM_011510554 PTGER1 5731 NM_000955 NOS1AP 9722 NM_001126060; NM_001164757; NM_014697 SORL1 6653 NM_003105 KCNE2 9992 NM_172201; NM_005136 SNURF 8926 NM_022804; NM_005678; NM_001394334 ZNF721 170960 NM_133474 SLC35E2 9906 NM_182838; NR_173244; NR_173245; NM_001199787 SELENBP1 8991 NM_001258289; XR_002957987; XR_921993; NM_003944; XM_024450671; NM_032183; NM_001258288 ARSB 411 XR_001742066; XM_011543393; XM_011543390; XM_017009471; XR_001742065; NM_198709; XM_011543392; XM_011543391; NM_000046 ZNF148 7707 NM_001348427; NM_001348436; NM_001348426; NM_001348430; NM_001348434; NM_001348425; NM_001348432; NM_001348431; NM_001348433; NM_001348424; NM_001348429; NM_021964; NM_001348428 ACTG2 72 NM_001199893; NM_001615 CXXC1 30827 XM_011525940; XM_017025718; XM_011525941; XM_017025719; NM_001101654; NM_014593 SETD1A 9739 NM_014712; XM_006721106; XM_024450499; XM_005255723; XM_017023909 EMD 2010 XM_024452349; NM_000117 ADM2 79924 NM_001369882; NM_001253845; NM_024866 F2RL3 9002 NM_003950; XM_005260139 PSCA 8000 NR_033343; NM_005672 CES3 23491 NM_001185176; NM_001185177; NM_024922; NM_012122 NOX1 27035 NM_007052; NM_013955; XM_017029407; NM_001271815; NM_013954 APIP 51074 XM_011520154; NM_015957; XM_017017875 HARS2 23438 NM_001363535; NM_001278731; NM_012208; NM_001278732; NM_001363536 C12orf10 60314 NM_021640 SOX18 54345 NM_018419 MYO7A 4647 XM_011545044; XR_001747889; XM_017017783; NM_001369365; XM_011545046; XM_017017782; XM_017017786; NM_000260; XM_011545050; XM_017017788; XM_017017781; XR_001747886; XM_017017787; XR_001747885; NM_001127180; NM_001127179; XM_017017778; XM_017017785; XM_017017784; XM_017017779; XM_017017780; XR_001747887; XR_001747888 SLC26A2 1836 XM_017009191; NM_000112 PNPLA6 10908 NM_001166114; NM_006702; NM_001166112; NM_001166113; NM_001166111 FAM3A 60343 XM_005274716; XM_005277879; XM_017029701; XM_024452419; NM_001171134; NM_001282311; XM_024452416; XR_002958798; XR_002958799; XR_002958803; NM_001171132; NM_001282312; NM_021806; XM_024452415; XR_002958801; NM_001363822; XR_002958800; XM_006724832; XM_006724833; XM_024452420; NM_001171133; XM_017029700; XM_017029702; XM_024452418; XR_002958802 SLC29A1 2030 XM_005248879; XM_005248882; NM_001078175; NM_001078177; NM_001078174; NM_001304466; NM_001304463; NM_004955; XM_005248880; XM_005248878; XM_011514341; NM_001372327; XM_024446348; NM_001304462; NM_001304465; XM_005248881; XM_005248876; NM_001078176 ZNF205 7755 NM_001042428; NM_001278158; XM_005255558; NM_003456 Stomach_Adenocarcinoma EFHC1 114327 NR_033327; NM_001172420; NM_018100 KCNN3 3782 NM_001204087; NM_001365837; NM_001365838; NM_170782; NM_002249 USP49 25862 NM_001286554; NM_018561; NM_001384542 ACTL6B 51412 NR_134539; NM_016188 RBM38 55544 NM_017495; NM_001291780; XM_011528885; XM_005260446; NM_183425 CNNM1 26507 NM_001345888; XM_011539631; XR_002956974; NM_020348; NM_001345887; NM_001345889; NR_144311; XR_945667 DRAP1 10589 NM_006442 CWF19L1 55280 NM_001303406; NM_018294; NM_001303407; NM_001303404; NM_001303405 ADAM12 8038 XM_017016705; NM_001288973; NM_001288974; NM_001288975; XM_017016706; NM_003474; NM_021641; XM_024448210 TSPAN6 7105 XM_011531018; NM_001278741; NM_001278743; NM_001278740; NM_001278742; NM_003270 TAF6L 10629 NM_006473; XM_017017100; XM_005273714 RHBDF1 64285 XM_017023556; XM_017023557; XM_017023558; NM_022450; XM_005255494; XM_005255498; XM_006720921 ZNF135 7694 XM_017027242; NM_001289401; NM_007134; NM_001164530; XM_017027241; XM_006723362; XM_017027240; XM_005259211; NM_001164527; XM_006723363; NM_003436; NM_001164529; NM_001289402 HOXD12 3238 NM_021193 FABP1 2168 NM_001443 PFN2 5217 NM_053024; NM_002628 GAST 2520 NM_000805 PPM1G 5496 NM_177983 ALDH8A1 64577 NM_001193480; NM_022568; NM_170771 NRSN2 80023 XM_017028074; XM_017028076; NM_001323685; XM_011529360; NM_001323679; NM_001323684; NM_024958; NM_001323680; NR_136649; XM_017028075; XM_011529363; XM_006723630; NM_001323682; NM_001323683; XM_017028073; NM_001323681; XM_011529362 DRD4 1815 NM_000797 GKN1 56287 NM_019617 PLA2G12A 81579 NM_030821 VWF 7450 NM_000552 A4GNT 51146 XM_017006543; NM_016161; XM_017006544 ANGEL2 90806 XM_005273345; XR_001737529; XM_005273344; XM_017002776; XR_001737527; NM_001300753; NM_001300757; NM_144567; XM_005273346; XM_017002778; XR_001737530; XR_001737531; XR_001737532; XM_005273347; XR_001737528; XR_247045; XM_017002774; XM_017002777; NR_125333; NM_001300758; NM_001300755; XM_017002775 PTPRCAP 5790 NM_005608 MAGEA10 4109 NM_001251828; NM_021048; NM_001011543 RGS12 6002 XM_017008534; XM_017008531; NM_001394162; NM_002926; NM_198227; NM_198229; NM_198432; NM_198587; NM_001394158; NM_001394159; XM_017008529; XR_924987; NM_001394156; NM_001394163; XM_011513543; XR_002959745; NM_001394154; NM_001394161; NM_198230; XR_427479; NM_001394157; NM_198430; NM_001394155 SRC 6714 XM_017028025; XM_017028026; XM_017028024; XM_011529013; NM_198291; XM_017028027; NM_005417 SLC5A3 6526 NM_006933 HSPB7 27129 NM_001349685; NM_001349688; NM_001349686; NM_001349683; NM_001349682; NM_001349689; NM_001349687; NM_014424 ZC3H3 23144 XM_006716536; XM_017013248; XM_011516944; XM_017013249; XR_928313; XM_011516943; NM_015117 TSSC4 10078 XM_011519830; NM_005706; NM_001297659; XM_006718118; NM_001297661; NM_001297660; NM_001297658 ADAM15 8751 NM_003815; NM_207191; NR_048577; NR_048578; NM_207197; NM_001261464; NM_207196; NM_207195; NR_048579; NM_001261466; NM_001261465; NM_207194 CTF1 1489 XM_011545759; NM_001330; XM_011545760; NR_165660; NM_001142544 TMEM120B 144404 XM_024448851; XM_024448852; NM_001080825 CA12 771 NM_001218; NR_135511; NM_206925; NM_001293642 DBN1 1627 NM_001393631; XM_017009139; NM_004395; XM_011534447; NM_080881; XM_017009140; NM_001363541; NM_001364151; NM_001364152; NM_001393630 CXCL5 6374 NM_002994 CSPG4 1464 NM_001897 FAHD2B 151313 XM_011510746; XM_011510747; XM_024452730; XM_024452731; XR_001738649; XR_002959246; XM_017003471; NM_001320849; XM_011510748; XM_011510745; XM_011510750; XM_017003470; XM_017003472; NM_001320848; NM_199336 KIR3DL2 3812 XM_017026784; XM_011526940; NM_006737; NM_001242867 IGLL1 3543 NM_001369906; NM_020070; NM_152855 CFP 5199 XM_017029575; NM_001145252; NM_002621 IL11 3589 NM_000641; NM_001267718 VEGFB 7423 NM_003377; NM_001243733 PGA5 5222 NM_014224 AR 367 NM_001348064; NM_001011645; NM_001348061; NM_001348063; NM_000044 GGA2 23062 XM_024450200; XM_017023075; NM_015044; NM_138640 LIPF 8513 NM_004190; NM_001198829; NM_001198830; NM_001198828; XM_011540311 MYH11 4629 XM_017023250; NM_002474; NM_022844; NM_001040113; NM_001040114; XM_011522502 CETP 1071 XM_006721124; NM_000078; NM_001286085 LRFN3 79414 NM_024509 CPSF4 10898 XM_011515757; XM_017011701; XM_017011702; XM_011515755; NM_001318161; NM_001318160; NM_006693; NM_001081559; NM_001318162; XM_011515756; XM_017011700; XM_017011703 GSDMD 79792 NM_024736; XM_011517301; NM_001166237 WT1 7490 NM_000378; NR_160306; NM_001367854; NM_001198551; NM_001198552; NM_024424; NM_024426; NM_024425 SATB2 23314 NM_015265; NM_001172517; XM_024452767; XM_024452768; NM_001172509; NR_134967; XM_005246396; XM_011510840; XM_017003656 PRLR 5618 XM_011514068; NM_001204315; XM_017009645; NM_001204318; XM_024446132; NM_001204317; NR_037910; NM_000949; NM_001204316; XM_006714484; XM_011514069; NM_001204314; XM_024446131 HOXA7 3204 NM_006896 KLHL11 55175 NM_018143; XR_001752552 TJAP1 93643 XM_006715254; XM_011514995; NM_001146017; NM_001146018; NM_001350570; NM_001394543; XM_006715257; XM_017011493; XR_926337; NM_001350565; NM_001350568; NM_001394542; NM_001394544; XM_006715250; XM_006715261; XM_006715268; XM_024446587; NM_001350562; XM_017011492; NM_001146020; NM_001350561; NM_001394538; NM_001394541; XM_017011489; XM_024446584; NM_001350566; NM_001350569; NM_080604; XM_006715262; XM_006715263; XM_006715266; XM_024446586; NM_001146016; NM_001350563; NM_001350564; NM_001394539; NM_001394545; XM_006715269; XM_011514996; XM_024446585; NM_001350567; XM_006715251; XM_006715265; XM_006715267; NM_001146019; NM_001394540; NR_146793 L1TD1 54596 NM_001164835; NM_019079 PTPRD 5789 XM_006716835; XM_017014958; XM_017014963; XM_017014968; XM_017014976; XM_017014987; XM_017014988; XM_017014990; NM_001040712; NM_001377947; NM_130391; XM_006716827; XM_006716832; XM_017014970; XM_017014971; XM_017014983; XM_017014985; XM_017014989; NM_001378058; XM_017014960; XM_017014965; XM_017014967; XM_017014979; NM_001377958; XM_017014964; XM_017014974; XM_017014977; XM_017014978; XM_017014986; NM_001377946; NM_002839; NM_130392; XM_006716834; XM_006716837; XM_017014959; XM_017014966; XM_017014984; XM_017014993; XM_017014995; NM_130393; XM_006716833; XM_017014972; XM_017014980; XM_017014981; XM_017014991; XM_024447625; XM_024447627; XM_011517992; XM_017014961; XM_017014969; XM_017014982; XM_017014994; XM_017014992; NM_001171025; XM_006716817; XM_006716823; XM_006716825; XM_017014973; XM_017014975 DAGLA 747 XM_017018239; XM_017018238; NM_006133; XM_017018240 CSF1 1435 NM_000757; NM_172210; XM_017000369; NM_172211; NM_172212 C1orf61 10485 NM_001320454; NR_135260; NR_168070; NR_168072; NR_135267; NR_168071; NR_168073; NM_001320455; NR_135265; NR_135264; NR_135266; NM_001320453; NM_006365; NR_135268; NR_135261; NR_135262; NR_135263 FOXRED2 80020 NM_001102371; NM_024955; NM_001363041; NM_001363042 HSD17B6 8630 XM_024449251; XM_011538927; XM_005269208; XM_011538925; XM_011538926; XM_024449250; XM_005269207; NM_003725; XM_005269209; XM_006719672; XM_024449249 FAIM2 23017 XM_005268730; NM_012306 SORBS1 10580 XM_017015501; XM_017015503; XM_017015510; XM_017015511; XM_017015512; XM_017015539; NM_001034957; NM_001290296; NM_001290297; NM_001290298; NM_001377208; NM_001377209; NM_001384448; NM_001384453; NM_001384456; NM_001384461; XM_006717589; XM_011539155; XM_017015500; XM_017015505; XM_017015509; XM_024447770; NM_001290294; NM_001384450; NM_001384460; NM_015385; NM_024991; XM_011539150; XM_017015506; XM_017015536; XM_024447769; NM_001377206; NM_001384452; NM_001384459; NM_001384463; XM_011539167; XM_017015514; XM_017015515; NM_001290295; NM_001377200; NM_001377207; NM_001384455; NM_001384464; XM_017015504; NM_001034954; NM_001034955; NM_001377201; NM_001384447; NM_001384449; NM_001384457; NM_001384458; NM_006434; XM_011539140; XM_017015502; XM_017015513; XM_017015523; XM_017015525; XM_017015537; XM_017015540; NM_001034956; NM_001377198; NM_001377205; NM_001384462; XM_017015507; XM_017015508; XM_017015517; XM_017015530; XM_017015532; XM_017015533; NM_001377199; NM_001377203; NM_001377204; NM_001384451; NM_001384454; NM_001384465; NM_001377197; NM_001377202 ERF 2077 XM_017026469; NM_001308402; NM_001312656; NM_006494; XM_017026468; NM_001301035 KIAA0907 22889 NM_014949 CD207 50489 XM_011532876; XM_011532875; XM_011532874; NM_015717 SF3A2 8175 NM_007165 AQP5 362 NM_001651; XM_005268838 GABRE 2564 XM_024452360; NM_021990; NM_021984; XM_011531140; XM_017029388; XM_017029389; NM_004961; NM_021987; XM_017029387 RAB40AL 282808 NM_001031834 F7 2155 XM_011537476; XM_011537475; NM_001267554; XM_011537474; NR_051961; XM_006719963; NM_019616; NM_000131 ZNF467 168544 NM_001329856; XM_005249959; XM_005249960; XM_017011799; NM_207336; XM_005249961; XM_011515858; XM_006715864; XM_011515857 HTR2A 3356 NM_001378924; NM_000621; NM_001165947 MAPRE3 22924 XM_011532700; NM_001303050; XM_006711967; XM_017003597; NM_012326 LY6G5C 80741 NM_025262; NM_001002849; NM_001002848 DAZ4 57135 XM_011531509; NM_020420; NM_001388484; NM_001005375; XM_011531510 MTTP 4547 NM_001300785; NM_001386140; NM_000253 CD7 924 XM_011523608; XM_017025316; NM_006137; XR_001752681; XR_001752680 ISG20 3669 NM_002201; NM_001303234; NM_001303236; XM_005254899; XM_006720488; XM_017022148; NM_001303235; NM_001303237; XM_011521521; NR_130134; XM_017022147; NM_001303233 ZSCAN2 54993 XM_024449978; XM_017022393; XM_024449975; NM_017894; NM_181877; XM_024449977; XM_024449976; NM_001007072 CCNL2 81669 XM_024450050; NM_001350499; XR_001737454; XR_946769; NM_001350497; NM_001350500; NR_146722; NM_001320153; NM_001320155; NM_030937; XM_017002420; XR_001737453; XR_002957676; XR_002957678; XR_002957684; NM_001350498; NM_001144867; XR_001737452; XR_001737455; NM_001039577; NR_135154; XM_024450049; XR_001737450; XR_426630; NR_146723; XM_011542216; XR_002957683; NM_001144868 MMP23B 8510 XM_017002617; XR_002957848; XM_017002615; NM_006983 GPA33 10223 XM_017000005; NM_005814 ITPKA 3706 XM_011521522; NM_002220 GPR162 27239 NM_014449; NM_019858 PGA3 643834 NM_001079807 RNF25 64320 XM_017004695; NM_022453 EPN1 29924 NM_001130072; NM_001321263; NM_013333; NM_001130071 PIK3C2G 5288 XM_017019472; XM_017019476; XM_017019470; XM_017019473; XR_931307; XM_017019475; NM_001288772; XM_011520696; XM_011520697; XM_017019471; NM_001288774; NM_004570; XM_017019474; XM_017019477; XM_011520700; XM_011520701; XM_017019478; XM_017019479 CLCN4 1183 NM_001256944; NM_001830 FLOT2 2319 XM_017024394; XM_024450667; XM_017024396; NM_004475; XM_017024395; XM_024450666; NM_001330170; XM_005257953 CACNA1H 8912 XM_006720965; XM_017023820; XM_006720963; XM_006720967; XM_011522724; XR_002957850; XM_005255652; XM_017023821; XM_011522727; XM_017023819; NM_021098; XM_006720968; XM_006720964; NM_001005407 ANXA10 11199 XM_011531571; NM_007193 NOTCH2NL 388677 NM_001395232; NM_001364006; NM_203458; NM_001395231 ADRA1D 146 NM_000678 SLC2A6 11182 XR_001746173; XM_011518189; XM_017014238; NM_001145099; XM_017014237; XR_001746175; XR_001746172; XM_017014236; XR_001746174; NM_017585 SIPA1 6494 XR_247210; NM_153253; XM_005274189; NM_006747 TMEM160 54958 NM_017854 PRDM16 63976 NM_199454; NM_022114 GTPBP6 8225 XM_011546184; XM_011545637; NM_012227; XM_006724447; XM_006724868 TP53I11 9537 NM_001258321; XM_011520478; XM_017018580; NM_001076787; NM_001258323; NM_001318387; NM_001318388; XM_017018581; XM_024448777; NM_001258320; NM_001258324; NM_001318390; NM_006034; NR_134612; XM_011520476; XM_011520475; NM_001318385; NM_001318386; NM_001318389; XM_005253227; XM_011520477; NM_001258322; XM_005253229; NM_001318384 PRRX2 51450 XM_017014803; NM_016307 ADAMTSL4 54507 XM_011509650; XR_001737242; XM_011509648; NM_001378596; XM_011509645; XM_011509652; NM_001288607; XM_011509651; NM_019032; XM_011509649; XM_017001506; XM_011509644; XM_017001507; NM_001288608; XR_921844; NM_025008 PALM 5064 XM_005259565; NM_002579; XM_005259566; XM_017026850; NM_001040134 RNF31 55072 NM_017999; NM_001310332 CLPTM1 1209 NM_001294; NM_001282175; NM_001199468; NM_001282176 CDC14A 8556 NM_033313; NM_001319212; NM_033312; NM_001319211; NM_001319210; NM_003672 NEBL 10529 XM_005252343; NM_001173484; NM_001377323; NM_001377327; XM_011519291; XR_001746996; XR_242691; NM_001377325; NM_001377324; NM_001377326; NM_213569; NM_001010896; NM_001377328; XM_005252344; NM_001377322; NM_001177483; XR_001746995; XM_005252342; XM_017015468; NM_006393; NM_016365 AQP8 343 NM_001169; XM_011545822; XM_011545823 NOL6 65083 NM_022917; NM_130793; XM_017015044; NM_139235 LMF2 91289 NM_001363816; XR_001755368; XR_938349; NM_033200; XM_017029077; XM_006724427; XM_006724426 FBP2 8789 NM_003837 GTPBP2 54676 XM_017010976; XM_024446478; XM_024446475; NM_001286216; XM_024446477; XM_024446476; NM_019096 GNL3L 54552 NM_001184819; NM_019067 FBLN1 2192 NM_006485; NM_006486; NM_001996; NM_006487 DDA1 79016 NM_024050; XM_024451701 ELOVL4 6785 NM_022726 ITGA10 8515 XM_017002623; XR_001737503; XM_017002626; XM_017002628; NM_001303041; NM_001303040; XR_001737502; XM_017002622; XM_017002625; NM_003637; XR_001737501; XR_001737504; XM_005277436; XM_017002624; XM_011510083; XM_011510084; XM_017002627 HOXB9 3219 NM_024017 PAX8 7849 NM_013992; NM_013953; NM_013952; NM_003466; NM_013951 GPR137 56834 XM_017018016; NM_001378083; XR_002957154; NM_001378078; NM_001378081; NM_001378087; XM_011545168; XM_005274100; NM_001170881; NM_001378076; NM_001378079; NM_001378085; NM_001378088; NM_001378089; NM_020155; XM_005274102; NM_001170880; NM_001378077; NM_001378082; NR_165394; NR_165396; XM_024448611; NM_001378086; NR_165397; XM_005274104; XM_011545169; NM_001177358; NM_001170726; NM_001378080; NM_001378084; NR_165395 APBB3 10307 NM_133174; NM_133172; NM_133173; NM_133176; NM_133175; NM_006051 SCGB2A1 4246 NM_002407 MAP4K2 5871 XR_002957155; XM_017018093; XM_024448634; XM_017018095; XM_024448630; NM_001307990; XM_024448629; NM_004579; XM_024448631; XM_024448633; XM_011545204 ZBTB10 65986 NM_001277145; NM_023929; NM_001105539 CLCA1 1179 NM_001285 GSTM1 2944 XM_005270782; NM_146421; NM_000561 CLDN5 7122 NM_001363066; NM_001363067; NM_001130861; NM_003277 MAPK3 5595 XR_243293; NM_001109891; NM_001040056; NM_002746 ZNF428 126299 NM_182498 LYL1 4066 NM_005583 GGT5 2687 XM_017028769; NM_001302464; XM_011530137; XM_017028768; NM_001099781; XM_011530134; XM_011530133; XM_011530135; NM_001302465; XM_005261557; XM_011530136; NM_001099782; NM_004121; XM_005261558 FAM124B 79843 NM_001122779; NM_024785 MTG1 92170 NM_138384 ALPL 249 NM_001177520; NM_001369803; NM_001127501; NM_001369804; NM_001369805; XM_017000903; NM_000478 SLC26A3 1811 NM_000111 TMEM127 55654 NM_001193304; XM_017004452; NM_017849; NM_032218; XM_017004450 EPOR 2057 NR_033663; NM_000121 FBXO17 115290 NR_104026; NM_148169; NM_024907 GALNT14 79623 NM_001253827; XR_001738942; XR_001738941; NM_001329095; XM_017004907; NM_001253826; XR_001738943; XM_017004906; NM_001329097; NM_001329096; NM_024572 RAB11B 9230 NM_004218 CCDC106 29903 NM_001370468; NM_001370467; NM_001370469; NM_001370470; NM_013301; NM_001370471 PCCA 5095 XM_017020609; XM_017020613; XM_017020616; NM_001178004; NR_148030; XM_017020611; XR_001749567; XR_001749568; XR_001749569; NM_001352606; NM_001352610; NM_001352611; NM_001352605; NR_148028; XM_017020615; NM_001352607; NM_001352609; XM_017020607; XR_001749574; XR_931615; NR_148029; XM_011521093; XM_017020605; NM_001352608; NM_001352612; XM_017020606; XR_001749577; NR_148027; XM_017020612; XR_001749576; NM_000282; NM_001127692; NR_148031 GJC1 10052 XM_024450525; XM_005256920; NM_005497; XM_024450526; XM_024450527; XR_934346; NM_001080383 TMEM158 25907 NM_015444 PGC 5225 NM_002630; NM_001166424 IFNA8 3445 NM_002170 HSPB6 126393 NM_144617 CLDN18 51208 NM_001002026; NM_016369 GATA4 2626 NM_001308093; NM_002052; NM_001308094; NM_001374273; NM_001374274 EPB41L2 2037 XM_017010353; XR_001743213; XR_001743215; NM_001350314; XM_011535527; XM_017010352; NM_001135555; NM_001350302; XM_011535525; XM_017010351; XM_017010356; NM_001350305; NM_001350309; NR_146620; XM_017010364; XR_001743216; XR_001743217; NM_001199389; NM_001350301; NM_001350303; NM_001350308; NM_001350312; XM_011535524; NM_001135554; NM_001252660; NM_001350307; NM_001350315; NM_001199388; NM_001350310; NM_001350311; NM_001431; NM_001350306; NM_001350320; XM_011535528; XM_017010350; XM_024446349; NM_001350299; NM_001350304; NM_001350313 TNNT2 7139 XM_011509943; NM_001001430; XM_011509946; XM_017002217; XM_011509941; XM_024449450; XM_024449455; NM_001001432; XM_006711508; XM_011509939; XM_017002216; XM_006711509; XM_011509942; NM_000364; NM_001276346; NM_001276347; XM_011509944; NM_001001431; XM_011509938; XM_011509940; XM_024449454; NM_001276345 ZNF557 79230 NM_024341; NM_001044387; NM_001044388 CDR2L 30850 NM_014603; XM_006721852 LRRC37A2 474170 XM_011524841; XM_011524849; XM_011524850; XM_011524844; XM_011524842; XM_024450774; XM_024450773; NM_001006607; XM_011524846; XM_024450775; NM_001385803; XM_011524843; XM_011524848 ZNF771 51333 NM_016643; NM_001142305 SERPIND1 3053 NM_000185 PAOX 196743 NM_152911; NM_207125; NM_207126; NR_109764; NM_207129; NM_207127; NR_109763; NR_109765; NM_207128; NR_109766 PITX1 5307 NM_002653 RET 5979 NM_020975; NM_001355216; NM_020630; NM_020629; NM_000323 CNGA3 1261 XM_006712243; NM_001298; NM_001079878; XM_011510554 PTGER1 5731 NM_000955 NOS1AP 9722 NM_001126060; NM_001164757; NM_014697 SORL1 6653 NM_003105 KCNE2 9992 NM_172201; NM_005136 SNURF 8926 NM_022804; NM_005678; NM_001394334 ZNF721 170960 NM_133474 SLC35E2 9906 NM_182838; NR_173244; NR_173245; NM_001199787 SELENBP1 8991 NM_001258289; XR_002957987; XR_921993; NM_003944; XM_024450671; NM_032183; NM_001258288 ARSB 411 XR_001742066; XM_011543393; XM_011543390; XM_017009471; XR_001742065; NM_198709; XM_011543392; XM_011543391; NM_000046 ZNF148 7707 NM_001348427; NM_001348436; NM_001348426; NM_001348430; NM_001348434; NM_001348425; NM_001348432; NM_001348431; NM_001348433; NM_001348424; NM_001348429; NM_021964; NM_001348428 ACTG2 72 NM_001199893; NM_001615 CXXC1 30827 XM_011525940; XM_017025718; XM_011525941; XM_017025719; NM_001101654; NM_014593 SETD1A 9739 NM_014712; XM_006721106; XM_024450499; XM_005255723; XM_017023909 EMD 2010 XM_024452349; NM_000117 ADM2 79924 NM_001369882; NM_001253845; NM_024866 F2RL3 9002 NM_003950; XM_005260139 PSCA 8000 NR_033343; NM_005672 CES3 23491 NM_001185176; NM_001185177; NM_024922; NM_012122 NOX1 27035 NM_007052; NM_013955; XM_017029407; NM_001271815; NM_013954 APIP 51074 XM_011520154; NM_015957; XM_017017875 HARS2 23438 NM_001363535; NM_001278731; NM_012208; NM_001278732; NM_001363536 C12orf10 60314 NM_021640 SOX18 54345 NM_018419 MYO7A 4647 XM_011545044; XR_001747889; XM_017017783; NM_001369365; XM_011545046; XM_017017782; XM_017017786; NM_000260; XM_011545050; XM_017017788; XM_017017781; XR_001747886; XM_017017787; XR_001747885; NM_001127180; NM_001127179; XM_017017778; XM_017017785; XM_017017784; XM_017017779; XM_017017780; XR_001747887; XR_001747888 SLC26A2 1836 XM_017009191; NM_000112 PNPLA6 10908 NM_001166114; NM_006702; NM_001166112; NM_001166113; NM_001166111 FAM3A 60343 XM_005274716; XM_005277879; XM_017029701; XM_024452419; NM_001171134; NM_001282311; XM_024452416; XR_002958798; XR_002958799; XR_002958803; NM_001171132; NM_001282312; NM_021806; XM_024452415; XR_002958801; NM_001363822; XR_002958800; XM_006724832; XM_006724833; XM_024452420; NM_001171133; XM_017029700; XM_017029702; XM_024452418; XR_002958802 SLC29A1 2030 XM_005248879; XM_005248882; NM_001078175; NM_001078177; NM_001078174; NM_001304466; NM_001304463; NM_004955; XM_005248880; XM_005248878; XM_011514341; NM_001372327; XM_024446348; NM_001304462; NM_001304465; XM_005248881; XM_005248876; NM_001078176 ZNF205 7755 NM_001042428; NM_001278158; XM_005255558; NM_003456

TABLE 4 DNA feature descriptions. DNA Feature Notation DNA Feature Description GENENAME_mut Mutation in GENENAME. GENENAME_hotspots Hotspot present in the GENENAME. GENENAME_p.LETTERNUMBER Hotspot in GENENAME. present in protein LETTER at amino acid position NUMBER CNA_NUMBER1_NUMBER2 Normalized copy number of bin NUMBER2 on chromosome NUMBER1. LOH_NUMBER1_NUMBER2 LOH status of bin NUMBER2 on chromosome NUMBER1 CNA_LETTER_NUMBER1 LOH status of bin NUMBER2 on chromosome NUMBER1 LOH_LETTER NUMBER1 LOH of bin NUMBER2 on chromosome NUMBER1 GENENAME Normalized copy numbers of GENENAME fusion_GENENAME1_GENENAME2 fusion of GENENAME1 with GENENAME2 fusion_GENENAME1_anygene fusion of GENENAME1 with GENENAME2 fusion_GENENAME1_anygene fusion of GENENAME1 with GENENAME2 tmb tumor mutation burden ploidy ploidy of sample msi MicroSatellite Instability status

TABLE 5 DNA DNA Feature NCBI Gene ID NCBI Accession Number(s) Ovarian_Cancer TP53_mut 7157 NG_017013; NC_000017 CNA_3_19 — — LOH_17_0 — — LOH_5_14 — — LOH_6_15 — — LOH_22_4 — — CNA_3_5 — — CNA_13_3 — — LOH_2_8 — — LOH_17_3 — — LOH_q_22 — — CNA_19_3 — — CNA_16_0 — — CNA_19_0 — — CSMD2_mut 114784 NC_000001; NG_053181 CNA_1_0 — — CNA_10_7 — — CNA_1_9 — — CNA_8_9 — — CNA_18_0 — — LOH_5_15 — — LOH_p_3 — — CNA_7_14 — — LOH_18_6 — — CNA_8_10 — — CNA_16_7 — — LOH_17_7 — — LINC00229 414351 NC_000022 CNA_9_9 — — FBXW7_hotspots 55294 NC_000004; NG_029466 CNA_1_20 — — CNA_6_16 — — CNA_3_16 — — CNA_11_7 — — CNA_p_7 — — CNA_15_3 — — CNA_p_3 — — CNA_8_8 — — LOH_19_1 — — CNA_1_24 — — CNA_7_5 — — CNA_17_3 — — CNA_3_4 — — LOH_p_17 — — LOH_17_4 — — LOH_18_0 — — CNA_q_7 — — CNA_12_2 — — CNA_8_5 — — LOH_1_2 — — CNA_3_17 — — RPL22P1 125371 NC_000003; NG_009515; NG_028279 CNA_7_4 — — CNA_16_8 — — LOH_5_3 — — LOH_9_2 — — LOH_3_6 — — DNAH5_mut 1767 NC_000005; NG_013081 LOH_17_5 — — CNA_11_11 — — LOH_17_6 — — LOH_9_3 — — CNA_5_14 — — CNA_1_19 — — TP53_hotspots 7157 NG_017013; NC_000017 LOH_3_5 — — CNA_7_13 — — CNA_5_15 — — CNA_2_23 — — CNA_7_0 — — LOH_10_6 — — CNA_5_13 — — LOH_2_19 — — CNA_2_19 — — CNA_6_15 — — LOH_18_2 — — CNA_1_4 — — tmb — — CNA_3_11 — — CNA_11_8 — — CNA_4_17 — — LOH_1_13 — — CNA_10_12 — — LOH_11_8 — — CNA_2_17 — — ARHGAP35_mut 2909 NC_000019; NG_047014 LOH_3_4 — — SHD 56961 NC_000019 FKBP4 2288 NC_000012 PPP2R1A_hotspots 5518 NG_047068; NC_000019 CNA_18_2 — — CNA_9_12 — — CNA_12_1 — — CNA_16_5 — — LOH_3_7 — — CNA_9_0 — — LINC00501 100820709 NC_000003 CNA_1_15 — — CNA_16_6 — — CNA_1_3 — — LOH_4_17 — — CNA_9_13 — — LOH_8_0 — — LOH_6_16 — — LOH_1_1 — — LOH_10_7 — — RPSAP33 647158 NC_000003; NG_011277 CNA_p_10 — — CNA_2_18 — — CNA_2_20 — — TMIGD2 126259 NC_000019 Breast_Cancer CNA_1_17 — — CNA_20_5 — — CNA_12_5 — — CNA_3_1 — — CSMD3_mut 114788 NC_000008 CNA_11_9 — — LOH_11_10 — — CNA_16_4 — — CNA_q_13 — — CNA_12_12 — — CNA_3_7 — — LOH_9_11 — — CNA_8_0 — — CNA_3_19 — — CNA_8_12 — — CNA_4_14 — — LOH_17_0 — — LOH_11_9 — — LOH_5_6 — — CNA_5_7 — — CNA_21_3 — — LOH_11_2 — — MTCO1P28 107075169 NC_000016; NG_046427 LOH_5_8 — — LOH_16_5 — — CNA_5_2 — — CNA_22_4 — — LOH_6_13 — — CNA_p_18 — — LOH_p_10 — — APC_mut 324 NG_008481; NC_000005 CNA_3_5 — — LOH_17_3 — — CNA_17_6 — — LOH_17_1 — — CNA_9_2 — — CNA_4_18 — — LOH_22_2 — — LOH_11_12 — — COG7 91949 NC_000016; NG_021287 LOH_9_0 — — LOH_q_22 — — CNA_p_16 — — CNA_3_13 — — LOH_16_7 — — CNA_16_0 — — CNA_20_3 — — SNTB2 6645 NW_003315946; NC_000016 PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_1_0 — — LRP1B_mut 53353 NC_000002; NG_051023 CNA_10_11 — — CNA_10_8 — — CNA_8_9 — — CNA_4_2 — — CNA_1_16 — — LOH_18_5 — — CDH1_mut 999 NC_000016; NG_008021 MRTFB 57496 NC_000016 CNA_18_0 — — CNA_18_5 — — LOH_p_3 — — LOH_1_20 — — CNA_7_14 — — LOH_18_6 — — LOH_8_1 — — PIK3CA_p.H1047 5290 NC_000003; NG_012113 LOH_19_4 — — CNA_8_10 — — CNA_3_18 — — CNA_16_7 — — CNA_16_1 — — MUC16_mut 94025 NC_000019; NG_055257 CNA_3_10 — — CNA_10_9 — — RBFOX1 54715 NC_000016; NG_011881 LINC02182 101928880 NC_000016 LOH_1_11 — — FBXW7_hotspots 55294 NC_000004; NG_029466 CNA_1_20 — — CNA_9_9 — — CNA_4_9 — — CNA_q_18 — — CNA_1_14 — — CDKN2A_mut 1029 NC_000009; NG_007485 TNR_mut 7143 NC_000001; NG_050931 CNA_3_16 — — CNA_11_7 — — CNA_21_2 — — CNA_p_7 — — ZSCAN32 54925 NC_000016 LINC00254 64735 NC_000016; NT_187609 CNA_15_3 — — LOH_14_8 — — IDH1_p.R132 3417 NG_023319; NC_000002 LOH_4_9 — — CNA_8_11 — — CNA_p_3 — — KMT2D_mut 8085 NG_027827; NC_000012 CNA_7_8 — — LOH_3_8 — — DNAJA3 9093 NG_029866; NC_000016; NT_187608 CNA_6_4 — — CNA_1_1 — — LOH_1_0 — — LOH_11_3 — — CNA_8_6 — — CNA_8_8 — — CNA_13_5 — — CNA_1_18 — — LOH_6_1 — — LOH_6_8 — — CNA_7_15 — — CNA_7_11 — — CNA_q_l — — CNA_13_7 — — CNA_13_9 — — CNA_1_24 — — ADCY9 115 NG_011434; NC_000016 CNA_5_11 — — CNA_7_5 — — LOH_11_11 — — CNA_17_3 — — CNA_6_12 — — CNA_3_6 — — CNA_3_4 — — CNA_7_9 — — LOH_p_17 — — CNA_8_1 — — LOH_11_7 — — LOH_15_6 — — LOH_17_4 — — CNA_7_10 — — CNA_12_2 — — CNA_q_7 — — CNA_3_17 — — CNA_10_0 — — CNA_16_8 — — LOH_13_7 — — LOH_5_3 — — GLIS2 84662 NG_016391; NC_000016; NT_187608 CNA_q_16 — — CNA_7_1 — — LOH_9_1 — — LOH_9_2 — — CNA_5_1 — — LOH_3_6 — — CNA_12_11 — — CNA_8_2 — — TBL3 10607 NC_000016 LOH_22_3 — — LOH_10_1 — — CNA_8_13 — — LOH_3_0 — — LOH_12_8 — — LOH_17_5 — — CNA_9_1 — — CNA_11_11 — — CNA_1_22 — — LOH_17_6 — — LOH_9_12 — — DNAH5_mut 1767 NC_000005; NG_013081 CNA_11_12 — — CNA_6_8 — — LOH_9_3 — — CNA_3_2 — — CNA_1_19 — — KMT2C_mut 58508 NC_000007; NG_033948 CNA_2_2 — — CNA_p_17 — — TP53_hotspots 7157 NG_017013; NC_000017 LOH_3_5 — — LOH_q_9 — — CNA_2_23 — — CNA_1_2 — — CNA_3_0 — — CNA_19_0 — — TVP23CP2 261735 NC_000016; NG_002361 LOH_3_1 — — RB1_mut 5925 NG_009009; NC_000013 CNA_13_10 — — CNA_1_4 — — tmb — — KRAS_hotspots 3845 NC_000012; NG_007524 LOH_19_5 — — CACNA1C_mut 775 NW_018654718; NC_000012; NG_008801 CNA_7_3 — — SRL 6345 NC_000016 MAP3K1_mut 4214 NG_031884; NC_000005 CNA_11_8 — — CNA_4_17 — — LOH_1_8 — — CNA_10_1 — — LOH_18_7 — — CNA_1_23 — — CNA_6_10 — — LOH_q_16 — — CNA_17_1 — — CNA_15_4 — — CNA_10_12 — — CNA_17_5 — — CNA_6_13 — — CNA_2_17 — — CYB5B 80777 NC_000016 LOH_3_4 — — LOH_14_7 — — CNA_p_5 — — ST3GAL2 6483 NG_046942; NC_000016 CNA_9_7 — — CNA_22_2 — — CNA_16_2 — — CNA_5_3 — — CNA_12_7 — — CNA_3_12 — — LOH_10_12 — — CNA_q_20 — — CNA_6_1 — — CNA_9_12 — — CNA_12_1 — — CNA_18_7 — — CNA_16_5 — — LOH_19_0 — — MSRB1 51734 NC_000016 CNA_9_0 — — CNA_18_6 — — LOH_10_8 — — CNA_20_4 — — CNA_1_15 — — CNA_11_6 — — CNA_16_6 — — CNA_4_1 — — LOH_13_10 — — LOH_10_0 — — CNA_6_0 — — LOH_9_13 — — CNA_17_7 — — CNA_1_3 — — LOH_4_17 — — CNA_3_15 — — CNA_11_10 — — CNA_9_13 — — CNA_22_3 — — CNA_2_1 — — NTHL1 4913 NC_000016; NG_008412 CNA_5_0 — — BRAF_hotspots 673 NC_000007; NG_007873 LOH_16_8 — — CNA_1_21 — — LOH_8_0 — — CNA_11_3 — — LOH_6_16 — — LOH_13_9 — — CNA_19_5 — — CNA_3_14 — — CNA_4_3 — — CTNNB1_hotspots 1499 NC_000003; NG_013302 CNA_2_11 — — CNA_2_13 — — LOH_16_6 — — CNA_2_20 — — CNA_6_2 — — CNA_17_0 — — CNA_p_10 — — CNA_q_22 — — Squamous_Cell_Carcinoma CNA_8_0 — — CNA_19_1 — — CNA_22_4 — — GM2AP1 2761 NG_001130; NC_000003 LOH_17_3 — — CNA_6_9 — — CNA_10_7 — — CNA_7_14 — — CNA_3_18 — — CNA_p_7 — — IDH1_p.R132 3417 NG_023319; NC_000002 KMT2D_mut 8085 NG_027827; NC_000012 LOH_13_3 — — LOH_6_1 — — TRA2B 6434 NG_029862; NC_000003 LOH_11_11 — — CNA_17_3 — — CNA_3_4 — — LOH_18_0 — — LOH_3_12 — — CNA_2_22 — — CNA_q_16 — — msi — — LOH_9_12 — — CNA_1_2 — — CNA_19_0 — — HMCN1_mut 83872 NC_000001; NG_011841 CNA_1_4 — — LOH_19_5 — — CNA_3_11 — — NOTCH1_mut 4851 NG_007458; NC_000009 CNA_15_4 — — CNA_2_6 — — CNA_12_1 — — LOH_3_2 — — CNA_14_9 — — LOH_3_7 — — LOH_4_17 — — CNA_9_13 — — CNA_5_6 — — LOH_8_0 — — CNA_q_22 — — CNA_20_5 — — CNA_3_19 — — CNA_8_12 — — LOH_17_0 — — CNA_5_2 — — LOH_6_15 — — LOH_22_4 — — LOH_22_2 — — LOH_18_5 — — LOH_13_2 — — CNA_16_7 — — CNA_1_6 — — CNA_3_16 — — CNA_11_7 — — CNA_13_2 — — CNA_p_3 — — CNA_q_1 — — CNA_6_12 — — LOH_p_17 — — PBRM1_mut 55193 NG_032108; NC_000003 CNA_3_17 — — LINC01994 401103 NC_000003 LOH_1_5 — — CNA_5_1 — — CNA_11_12 — — CNA_2_2 — — CNA_5_15 — — LOH_3_1 — — NRAS_mut 4893 NG_007572; NC_000001 tmb — — KRAS_hotspots 3845 NC_000012; NG_007524 CNA_10_1 — — LOH_q_16 — — CNA_17_1 — — CNA_17_5 — — CNA_p_5 — — CNA_9_7 — — LOH_19_0 — — CNA_11_6 — — CNA_4_1 — — LINC00971 440970 NC_000003; NW_018654711 CNA_2_20 — — CNA_6_2 — — CNA_3_1 — — CNA_12_12 — — BRAF_p.V600 673 NC_000007; NG_007873 TRIM42 287015 NC_000003 CNA_7_7 — — CNA_21_3 — — APC_mut 324 NG_008481; NC_000005 CNA_13_3 — — LOH_17_1 — — LOH_3_18 — — LOH_9_0 — — CNA_1_0 — — CNA_10_11 — — CNA_18_0 — — MGA_mut 23269 NC_000015 LOH_5_13 — — CDKN2A_mut 1029 NC_000009; NG_007485 CNA_8_11 — — LOH_3_8 — — LOH_2_22 — — LOH_9_1 — — LOH_10_1 — — CNA_9_1 — — CNA_11_11 — — CNA_3_8 — — CNA_7_13 — — CNA_5_13 — — CNA_11_8 — — CNA_13_4 — — CNA_16_2 — — CNA_5_3 — — CNA_9_0 — — CNA_1_15 — — CNA_17_7 — — CNA_3_15 — — CNA_22_3 — — CNA_1_8 — — CNA_5_0 — — ARID1A_mut 8289 NC_000001; NG_029965 CNA_12_5 — — CNA_3_7 — — LOH_3_11 — — LOH_3_3 — — FAT1_mut 2195 NG_046994; NC_000004 SYNE1_mut 23345 NG_012855; NC_000006 CNA_p_16 — — LOH_16_7 — — CNA_19_3 — — CNA_14_8 — — SRRM1P2 100420834 NC_000003; NG_022252 CNA_18_5 — — CNA_5_16 — — CNA_5_17 — — KBTBD8 84541 NC_000003 CNA_7_15 — — VHL_mut 7428 NC_000003; NG_008212 LOH_17_4 — — CNA_q_7 — — LOH_15_6 — — LOH_5_16 — — CNA_8_13 — — LOH_17_5 — — CNA_6_8 — — SPTA1_mut 6708 NC_000001; NG_011474 CNA_1_19 — — CNA_p_17 — — CNA_6_10 — — HRAS_hotspots 3265 NT_187586; NG_007666; NC_000011 LOH_5_17 — — MAGEF1 64110 NC_000003 RYR2_mut 6262 NG_008799; NC_000001 CNA_9_10 — — CNA_9_12 — — CNA_20_4 — — BRAF_hotspots 673 NC_000007; NG_007873 CNA_19_5 — — CNA_3_14 — — CNA_p_10 — — Lung_Adenocarcinoma DPPA3P2 400206 NC_000014; NG_023379 CNA_20_5 — — CSMD3_mut 114788 NC_000008 CNA_11_9 — — CNA_16_4 — — CNA_12_12 — — CNA_q_13 — — CNA_19_1 — — CNA_3_19 — — PIK3CA_mut 5290 NC_000003; NG_012113 CNA_8_12 — — LOH_17_0 — — CNA_20_0 — — CNA_7_7 — — ZFHX4_mut 79776 NC_000008 CNA_5_2 — — LOH_6_13 — — CNA_22_4 — — CNA_p_18 — — NKX2-1 7080 NC_000014; NG_013365 LINC01511 100506791 NC_000005; NT_187547 LOH_1_9 — — LOH_2_23 — — APC_mut 324 NG_008481; NC_000005 LOH_17_3 — — CNA_17_6 — — LOH_17_1 — — DNAH2_mut 146754 NC_000017 LOH_3_18 — — LOH_22_2 — — SYNE1_mut 23345 NG_012855; NC_000006 EP300_mut 2033 NG_009817; NC_000022 LOH_9_0 — — LOH_q_22 — — CNA_p_16 — — SFTA3 253970 NC_000014 ADAMTS12_mut 81792 NT_187551; NC_000005 CNA_16_0 — — CNA_20_3 — — CNA_19_3 — — LOH_3_14 — — PIK3CA_hotspots 5290 NC_000003; NG_012113 EGFR_hotspots 1956 NG_007726; NC_000007 CNA_1_0 — — CNA_4_2 — — LOH_p_3 — — STK11_mut 6794 NG_007460; NC_000019 LOH_13_2 — — LOH_8_1 — — MGA_mut 23269 NC_000015 TMTC1_mut 83857 NC_000012 CNA_3_18 — — TTN_mut 7273 NC_000002; NG_011618 CNA_16_7 — — LOH_17_7 — — CNA_3_10 — — CNA_10_9 — — CDK8 1024 NC_000013 PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 CNA_9_9 — — CNA_1_20 — — CNA_4_9 — — CNA_5_16 — — CNA_3_16 — — CNA_21_2 — — CNA_p_7 — — RBM10_mut 8241 NG_012548; NC_000023 CNA_15_3 — — ZMYM2 7750 NG_023348; NC_000013 CNA_13_2 — — CNA_8_11 — — LOH_4_9 — — KMT2D_mut 8085 NG_027827; NC_000012 CNA_1_1 — — LOH_1_0 — — LOH_6_8 — — CNA_7_15 — — LOH_19_1 — — CNA_q_1 — — PTPRD_mut 5789 NC_000009; NG_033963 LOH_11_11 — — CNA_17_3 — — CNA_3_6 — — LOH_10_9 — — LOH_17_4 — — CNA_7_4 — — GABRB3 2562 NC_000015; NG_012836 LOH_2_22 — — KRAS_mut 3845 NC_000012; NG_007524 CNA_2_22 — — CNA_8_13 — — CNA_15_5 — — CNA_p_20 — — LOH_3_0 — — DNAH5_mut 1767 NC_000005; NG_013081 LOH_17_5 — — msi — — CNA_11_11 — — LOH_17_6 — — NKX2-8 26257 NC_000014 CNA_11_12 — — ZNF804A_mut 91752 NC_000002; NG_046950 CNA_3_8 — — CNA_11_1 — — CNA_1_19 — — RPL29P3 729042 NG_009496; NC_000014 CNA_2_23 — — CNA_7_0 — — LINC02248 107984780 NC_000015 CNA_1_2 — — CNA_19_0 — — LOH_4_15 — — CNA_6_15 — — LOH_18_2 — — CNA_q_8 — — USH2A_mut 7399 NC_000001; NG_009497 tmb — — CNA_11_8 — — LOH_1_3 — — LOH_1_8 — — CNA_6_10 — — LOH_q_16 — — CNA_15_4 — — LOH_p_18 — — CNA_6_13 — — LOH_11_8 — — CNA_p_5 — — CNA_9_7 — — RYR2_mut 6262 NG_008799; NC_000001 CNA_5_3 — — CNA_9_3 — — LOH_6_12 — — CNA_q_20 — — KEAP1_mut 9817 NC_000019 CNA_9_10 — — CNA_12_1 — — LOH_19_0 — — CNA_19_4 — — LOH_15_4 — — CNA_20_4 — — KRAS_p.G12 3845 NC_000012; NG_007524 CNA_1_15 — — CNA_11_6 — — LOH_10_2 — — CNA_6_0 — — CNA_1_3 — — CDH18-AS1 102725105 NC_000005 CNA_11_10 — — CNA_9_13 — — CNA_22_3 — — CNA_5_0 — — LOH_1_1 — — SETD2_mut 29072 NC_000003; NG_032091 CNA_3_14 — — CNA_4_3 — — CNA_p_10 — — CNA_2_13 — — CNA_17_0 — — CNA_q_22 — — LOH_11_1 — — CNA_2_20 — — Prostate_Adenocarcinoma CNA_1_17 — — CNA_20_5 — — LOH_4_16 — — CNA_11_9 — — CNA_16_4 — — CNA_q_13 — — CNA_3_7 — — BRAF_p.V600 673 NC_000007; NG_007873 CNA_8_0 — — CNA_20_0 — — LOH_13_5 — — LOH_5_7 — — CNA_5_7 — — LOH_17_0 — — CNA_7_7 — — CNA_21_3 — — LOH_11_2 — — LOH_3_3 — — LOH_6_13 — — LOH_16_5 — — CNA_22_4 — — LOH_6_15 — — LOH_p_10 — — LOH_22_4 — — CNA_3_5 — — CNA_13_3 — — CNA_17_6 — — LOH_17_1 — — CNA_9_2 — — CNA_4_18 — — LOH_11_12 — — CNA_p_16 — — CNA_19_3 — — CNA_20_3 — — CNA_6_14 — — PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_6_9 — — CNA_1_0 — — CNA_10_11 — — CNA_10_8 — — CNA_1_16 — — LOH_18_5 — — LOH_p_3 — — LOH_18_6 — — LOH_13_2 — — LOH_8_1 — — CNA_7_14 — — CNA_8_10 — — CNA_16_7 — — CNA_16_1 — — LOH_17_7 — — LOH_13_4 — — CNA_5_10 — — LOH_1_11 — — LOH_5_13 — — CNA_q_18 — — CNA_6_16 — — LOH_8_2 — — CNA_p_7 — — CNA_21_2 — — LOH_14_8 — — IDH1_p.R132 3417 NG_023319; NC_000002 CNA_13_2 — — LOH_16_4 — — CNA_p_3 — — CNA_1_1 — — LOH_1_0 — — LOH_6_14 — — CNA_8_6 — — CNA_8_8 — — CNA_1_18 — — LOH_6_1 — — LOH_6_8 — — CNA_7_15 — — CNA_q_1 — — CNA_13_7 — — CNA_1_24 — — CNA_5_11 — — IDH1_hotspots 3417 NG_023319; NC_000002 CNA_17_3 — — CNA_3_4 — — LOH_10_9 — — LOH_p_17 — — CNA_3_6 — — CNA_8_1 — — LOH_17_4 — — CNA_q_7 — — CNA_8_7 — — CNA_7_10 — — CNA_12_2 — — CNA_8_5 — — LOH_1_2 — — LOH_13_8 — — CNA_10_0 — — SPOP_hotspots 8405 NC_000017; NG_041815 CNA_16_8 — — CNA_q_16 — — LOH_9_1 — — LOH_14_3 — — LOH_9_2 — — CNA_8_2 — — LOH_22_3 — — CNA_8_13 — — CNA_p_20 — — CNA_9_1 — — LOH_17_5 — — CNA_10_4 — — CNA_11_11 — — CNA_1_22 — — LOH_17_6 — — LOH_9_7 — — CNA_11_1 — — CNA_6_8 — — CNA_11_12 — — ATM_mut 472 NC_000011; NG_009830 CNA_5_14 — — CNA_1_19 — — LOH_9_9 — — TP53_hotspots 7157 NG_017013; NC_000017 LOH_3_5 — — LOH_10_6 — — CNA_2_23 — — CNA_19_0 — — LOH_4_15 — — CNA_6_15 — — CNA_q_8 — — tmb — — CNA_7_3 — — LOH_19_5 — — CNA_11_8 — — CNA_4_17 — — LOH_18_7 — — LOH_q_16 — — CNA_20_1 — — CNA_10_12 — — CNA_17_5 — — LOH_2_2 — — LOH_10_11 — — LOH_3_4 — — LOH_14_7 — — CNA_p_5 — — CNA_9_7 — — CNA_16_2 — — LOH_10_12 — — CNA_q_20 — — CNA_18_7 — — CNA_9_11 — — LOH_10_8 — — CNA_12_1 — — CNA_16_5 — — LOH_19_0 — — LOH_3_7 — — CNA_6_1 — — CNA_19_4 — — CNA_1_15 — — CNA_11_6 — — GTF2I_p.L424 2969 NC_000007; NG_008179 LOH_10_0 — — CNA_6_0 — — CNA_17_7 — — LOH_14_9 — — CNA_11_10 — — CNA_4_16 — — CNA_5_0 — — BRAF_hotspots 673 NC_000007; NG_007873 LOH_16_8 — — LOH_8_0 — — LOH_6_16 — — LOH_1_1 — — CNA_19_5 — — LOH_10_7 — — CNA_1_11 — — ARID1A_mut 8289 NC_000001; NG_029965 CNA_2_13 — — LOH_11_1 — — CNA_6_2 — — LOH_6_9 — — CNA_17_0 — — COX4I1P2 652170 NG_011339; NC_000013 CNA_10_6 — — CNA_3_3 — — CNA_11_2 — — Neuroendocrine CNA_1_17 — — CNA_2_5 — — CNA_q_13 — — CNA_12_12 — — CNA_4_11 — — MAST4 375449 NC_000005; NG_034036 CNA_5_9 — — LOH_1_21 — — CNA_8_0 — — LOH_3_11 — — CNA_3_19 — — CNA_20_0 — — CNA_1_5 — — CNA_8_12 — — LOH_17_0 — — CNA_5_7 — — LOH_11_2 — — CNA_5_2 — — CNA_22_4 — — CNA_p_18 — — LOH_22_4 — — CNA_13_3 — — LOH_2_8 — — LOH_17_1 — — LOH_22_2 — — LOH_9_0 — — LOH_q_22 — — CNA_p_16 — — CNA_19_3 — — CNA_20_3 — — CNA_1_0 — — CNA_1_16 — — CNA_18_0 — — LOH_p_3 — — LOH_1_20 — — LOH_1_17 — — LOH_8_1 — — CNA_10_10 — — CNA_3_18 — — CNA_16_7 — — LOH_1_11 — — CNA_9_9 — — CNA_1_10 — — LOH_5_13 — — CNA_4_9 — — CNA_1_14 — — CNA_6_16 — — CNA_5_16 — — CNA_p_7 — — CNA_11_7 — — CNA_3_16 — — CNA_13_2 — — CNA_8_11 — — LOH_1_0 — — LOH_11_3 — — CNA_1_18 — — CNA_q_1 — — LOH_1_23 — — CNA_13_7 — — LOH_6_10 — — CNA_17_3 — — CNA_12_10 — — LOH_11_6 — — LOH_p_17 — — CNA_q_7 — — LOH_17_4 — — TMEM205 374882 NC_000019 CNA_3_17 — — LOH_1_2 — — CNA_16_8 — — CNA_q_16 — — CNA_2_8 — — CNA_5_1 — — CNA_12_11 — — CNA_12_6 — — CNA_8_13 — — CNA_p_20 — — CNA_5_8 — — LOH_17_6 — — CNA_5_14 — — CNA_1_19 — — CNA_p_17 — — CNA_1_2 — — CNA_19_0 — — CNA_12_8 — — tmb — — CNA_7_3 — — ADAMTS6 11174 NC_000005 LOH_1_8 — — CNA_1_23 — — ZBED3 84327 NC_000005 CNA_17_5 — — CNA_6_13 — — CNA_5_12 — — CNA_22_2 — — CNA_12_7 — — CNA_3_12 — — CNA_9_10 — — CNA_19_4 — — CNA_9_11 — — CNA_1_15 — — CNA_6_0 — — CNA_17_7 — — CNA_9_13 — — CNA_22_3 — — CNA_5_0 — — CNA_1_8 — — CNA_5_6 — — CNA_1_21 — — CNA_19_5 — — CNA_q_22 — — CNA_2_11 — — LOH_5_8 — — LOH_11_1 — — CNA_17_0 — — CNA_10_6 — — Pancreatic_Adenocarcinoma LOH_11_10 — — CNA_q_13 — — CNA_20_0 — — LOH_17_0 — — LOH_2_18 — — CNA_18_4 — — APC_mut 324 NG_008481; NC_000005 CNA_3_5 — — CNA_13_3 — — LOH_17_3 — — LOH_17_1 — — CNA_10_2 — — LOH_9_0 — — CNA_p_16 — — LOH_16_7 — — CNA_20_3 — — KRAS_p.Q61 3845 NC_000012; NG_007524 CNA_6_14 — — CNA_1_0 — — CNA_14_8 — — CNA_8_9 — — CNA_18_5 — — LOH_p_3 — — CNA_7_14 — — LOH_18_6 — — CNA_16_7 — — CNA_16_1 — — LOH_18_4 — — CNA_6_16 — — CNA_6_3 — — CDKN2A_mut 1029 NC_000009; NG_007485 CNA_3_16 — — CNA_p_7 — — CNA_p_3 — — CNA_8_6 — — CNA_13_5 — — LOH_19_1 — — CNA_q_1 — — CNA_1_24 — — CNA_17_3 — — CDKN2A_hotspots 1029 NC_000009; NG_007485 LOH_p_17 — — CNA_q_7 — — CNA_8_5 — — KRAS_mut 3845 NC_000012; NG_007524 CNA_q_16 — — LOH_9_1 — — CNA_12_6 — — CNA_p_20 — — CNA_9_1 — — LOH_17_5 — — CNA_6_8 — — LOH_9_3 — — CNA_p_17 — — TP53_hotspots 7157 NG_017013; NC_000017 CNA_7_0 — — CNA_3_0 — — CNA_18_3 — — CNA_6_15 — — CNA_q_8 — — LOH_18_2 — — tmb — — KRAS_hotspots 3845 NC_000012; NG_007524 CNA_7_3 — — SMAD4_mut 4089 NC_000018; NG_013013 LOH_1_8 — — LOH_18_7 — — LOH_q_16 — — CNA_20_1 — — CNA_17_1 — — CNA_17_5 — — CNA_4_8 — — CNA_6_11 — — CNA_22_2 — — CNA_18_2 — — LOH_10_12 — — CNA_q_20 — — CNA_6_1 — — CNA_18_7 — — CNA_12_1 — — CNA_14_9 — — CNA_9_0 — — CNA_20_4 — — CNA_18_6 — — CNA_1_15 — — KRAS_p.G12 3845 NC_000012; NG_007524 CNA_6_0 — — CNA_17_7 — — CNA_11_10 — — CNA_q_22 — — CNA_17_0 — — CNA_6_2 — — LOH_6_9 — — CNA_11_2 — — Gastrointestinal_Adenocarcinoma ACVR2A_mut 92 NC_000002 APC_mut 324 NG_008481; NC_000005 ARID1A_mut 8289 NC_000001; NG_029965 CDH1_mut 999 NC_000016; NG_008021 FAT1_mut 2195 NG_046994; NC_000004 FAT4_mut 79633 NG_033865; NC_000004 MED12_mut 9968 NG_012808; NC_000023 MED13L_mut 23389 NC_000012; NG_023366 MGAM_mut 8972 NT_187562; NC_000007; NG_033954 NSD1_mut 64324 NC_000005; NG_009821 PCDH17_mut 27253 NC_000013 PHKA1_mut 5255 NG_016599; NC_000023 PREX2_mut 80243 NG_047022; NC_000008 PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 SPTA1_mut 6708 NC_000001; NG_011474 STAG2_mut 10735 NC_000023; NG_033796 SYNE1_mut 23345 NG_012855; NC_000006 ZFHX3_mut 463 NG_013211; NC_000016 IDH1_p.R132 3417 NG_023319; NC_000002 SMAD4_hotspots 4089 NC_000018; NG_013013 NFE2L2_hotspots 4780 NC_000002 KRAS_hotspots 3845 NC_000012; NG_007524 PTEN_hotspots 5728 NC_000010; NW_013171807; NG_007466 HRAS_hotspots 3265 NT_187586; NG_007666; NC_000011 CNA_1_4 — — LOH_1_4 — — CNA_1_9 — — CNA_1_15 — — CNA_1_17 — — CNA_1_19 — — CNA_1_20 — — CNA_1_22 — — CNA_1_23 — — CNA_1_24 — — CNA_2_5 — — LOH_2_16 — — CNA_2_17 — — LOH_2_18 — — CNA_2_22 — — CNA_2_23 — — CNA_3_0 — — CNA_3_1 — — CNA_3_2 — — CNA_3_3 — — CNA_3_4 — — CNA_3_6 — — LOH_3_6 — — CNA_3_7 — — LOH_3_7 — — CNA_3_11 — — CNA_3_12 — — CNA_3_13 — — CNA_3_14 — — CNA_3_15 — — CNA_3_16 — — CNA_3_17 — — CNA_3_18 — — CNA_3_19 — — CNA_4_2 — — CNA_4_8 — — LOH_4_8 — — CNA_4_9 — — LOH_4_9 — — LOH_4_17 — — CNA_4_18 — — CNA_5_0 — — CNA_5_1 — — CNA_5_2 — — CNA_5_6 — — LOH_5_6 — — CNA_5_7 — — LOH_5_7 — — CNA_5_8 — — LOH_5_8 — — CNA_5_9 — — LOH_5_9 — — CNA_5_10 — — LOH_5_10 — — CNA_5_11 — — LOH_5_11 — — CNA_5_12 — — LOH_5_12 — — CNA_5_13 — — LOH_5_13 — — CNA_5_14 — — LOH_5_14 — — CNA_5_15 — — CNA_5_16 — — CNA_5_17 — — CNA_6_0 — — CNA_6_1 — — CNA_6_2 — — CNA_6_3 — — CNA_6_4 — — CNA_6_8 — — CNA_6_9 — — CNA_6_10 — — LOH_6_10 — — CNA_6_12 — — LOH_6_12 — — CNA_6_13 — — CNA_6_14 — — CNA_6_15 — — CNA_6_16 — — LOH_6_16 — — CNA_7_0 — — CNA_7_3 — — CNA_7_4 — — CNA_7_5 — — CNA_7_7 — — CNA_7_8 — — CNA_7_9 — — CNA_7_10 — — CNA_7_11 — — CNA_7_13 — — CNA_7_14 — — CNA_7_15 — — CNA_8_0 — — CNA_8_1 — — LOH_8_1 — — CNA_8_2 — — LOH_8_2 — — CNA_9_2 — — CNA_9_3 — — LOH_9_3 — — CNA_9_6 — — CNA_9_7 — — CNA_9_8 — — CNA_9_9 — — LOH_9_9 — — LOH_9_10 — — CNA_9_11 — — LOH_9_12 — — CNA_9_13 — — LOH_9_13 — — CNA_10_0 — — CNA_10_2 — — CNA_10_4 — — CNA_10_5 — — CNA_10_7 — — LOH_10_7 — — CNA_10_8 — — CNA_10_10 — — LOH_10_11 — — CNA_10_12 — — LOH_10_12 — — CNA_11_1 — — CNA_11_2 — — CNA_11_3 — — CNA_11_6 — — CNA_11_11 — — CNA_11_12 — — CNA_12_1 — — CNA_12_2 — — LOH_12_8 — — LOH_12_10 — — CNA_12_12 — — CNA_13_2 — — CNA_13_3 — — CNA_13_4 — — LOH_13_4 — — CNA_13_5 — — CNA_13_7 — — CNA_14_9 — — CNA_16_0 — — CNA_16_2 — — LOH_16_4 — — CNA_16_7 — — LOH_16_7 — — CNA_16_8 — — CNA_17_0 — — CNA_17_1 — — LOH_17_1 — — CNA_17_3 — — LOH_17_3 — — CNA_17_5 — — CNA_17_6 — — CNA_17_7 — — LOH_18_2 — — LOH_18_3 — — CNA_18_4 — — LOH_18_4 — — CNA_18_5 — — LOH_18_5 — — CNA_19_1 — — LOH_19_1 — — LOH_19_3 — — CNA_19_5 — — CNA_20_1 — — CNA_20_3 — — CNA_20_4 — — CNA_20_5 — — CNA_21_2 — — CNA_21_3 — — CNA_22_3 — — CNA_p_10 — — CNA_p_16 — — CNA_p_17 — — CNA_p_3 — — CNA_p_5 — — CNA_q_1 — — CNA_q_13 — — CNA_q_16 — — CNA_q_18 — — CNA_q_20 — — LOH_p_3 — — LOH_q_9 — — VDAC1P12 100874289 NG_032346; NC_000013 RPS28P8 100271381 NG_010096; NC_000013 MAPK6P3 317684 NG_002453; NG_029191; NC_000013 SPRYD7 57213 NC_000013 RPL18P10 100271286 NC_000013; NG_010943 VPS36 51028 NC_000013 LINC00393 100874156 NC_000013 ANKRD29 147463 NC_000018 LINC01543 100506787 NC_000018 KCTD1 284252 NG_054919; NC_000018 CIAPIN1P 728599 NG_054919; NC_000018; NG_008808 AQP4 361 NG_029560; NC_000018 CHST9 83539 NG_029856; NC_000018 LINC01908 105372037 NC_000018 UBA52P9 100271344 NC_000018; NG_011241 RBM22P1 400645 NG_023396; NC_000018 PA2G4P3 619212 NG_005881; NC_000018 CDH2 1000 NG_011959; NC_000018 ARIH2P1 390844 NG_009482; NC_000018 DSC3 1825 NC_000018; NG_016782 DSC2 1824 NC_000018; NG_008208 DSC1 1823 NC_000018; NG_029192 DSG3 1830 NC_000018 DSG2 1829 NC_000018; NG_007072 TTR 7276 NC_000018; NG_009490 B4GALT6 9331 NC_000018 LRRC37A7P 100421589 NC_000018; NG_026286 SLC25A52 147407 NC_000018 TRAPPC8 22878 NC_000018 PGDP1 342705 NG_022489; NC_000018 RNF125 54941 NG_042056; NC_000018 RNF138 51444 NC_000018; NG_029944 GAREM1 64762 NC_000018; NG_030329 MEP1B 4225 NC_000018 CLUHP6 100418754 NC_000018; NG_026287 HNRNPA1P7 388275 NG_005529; NG_030329; NC_000018 WBP11P1 441818 NC_000018 KLHL14 57565 NC_000018 CCDC178 374864 NC_000018 ASXL3 80816 NG_055244; NC_000018 NOL4 8715 NC_000018 DTNA 1837 NG_009201; NC_000018 MAPRE2 10982 NC_000018; NG_047123 ZNF271P 10778 NC_000018 ZNF24 7572 NC_000018 ZNF396 252884 NC_000018 INO80C 125476 NC_000018 fusion_FRS2_anygene 10818 NC_000012 fusion_SLC45A3_anygene 85414 NC_000001 fusion_TMPRSS2_anygene 7113 NC_000021; NG_047085 fusion_anygene_C12orf28 196446 NC_000012 fusion_anygene_CPM 1368 NC_000012 fusion_anygene_ERG 2078 NC_000021; NG_029732 fusion_anygene_RET 5979 NG_007489; NC_000010 fusion_TMPRSS2_ERG 7113; 2078 NC_000021; NG_047085 NC_000021; NG_029732 tmb — — ploidy — — msi — — Liver_Neoplasm BAP1_mut 8314 NG_031859; NC_000003 PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 SYNE1_mut 23345 NG_012855; NC_000006 TTN_mut 7273 NC_000002; NG_011618 ZFHX4_mut 79776 NC_000008 KRAS_p.G12 3845 NC_000012; NG_007524 PIK3CA_hotspots 5290 NC_000003; NG_012113 CTNNB1_hotspots 1499 NC_000003; NG_013302 BRAF_hotspots 673 NC_000007; NG_007873 KRAS_hotspots 3845 NC_000012; NG_007524 CNA_1_0 — — LOH_1_0 — — CNA_1_3 — — CNA_1_9 — — CNA_1_10 — — CNA_1_11 — — CNA_1_14 — — CNA_1_15 — — CNA_2_20 — — LOH_2_22 — — CNA_2_23 — — LOH_2_23 — — LOH_3_0 — — LOH_3_1 — — LOH_3_8 — — LOH_3_11 — — LOH_3_12 — — CNA_3_14 — — CNA_3_16 — — CNA_3_19 — — CNA_4_1 — — LOH_4_8 — — CNA_4_9 — — CNA_4_10 — — LOH_4_10 — — LOH_4_17 — — CNA_4_18 — — CNA_5_0 — — CNA_5_7 — — LOH_5_7 — — CNA_5_9 — — LOH_5_9 — — CNA_5_13 — — CNA_5_16 — — LOH_5_16 — — CNA_6_0 — — LOH_6_1 — — CNA_6_2 — — CNA_6_4 — — CNA_6_10 — — LOH_6_14 — — LOH_6_16 — — CNA_7_3 — — CNA_8_0 — — CNA_8_1 — — LOH_8_1 — — LOH_8_2 — — CNA_8_10 — — CNA_8_13 — — LOH_9_0 — — LOH_9_1 — — LOH_9_3 — — CNA_9_11 — — CNA_9_12 — — CNA_10_1 — — LOH_10_2 — — LOH_10_4 — — LOH_10_12 — — LOH_11_1 — — CNA_11_7 — — LOH_11_11 — — CNA_12_1 — — CNA_13_3 — — CNA_13_4 — — CNA_13_6 — — CNA_15_4 — — LOH_15_4 — — CNA_16_0 — — CNA_16_1 — — CNA_16_2 — — CNA_16_7 — — LOH_16_8 — — CNA_17_0 — — LOH_17_4 — — LOH_17_5 — — LOH_17_7 — — LOH_18_3 — — CNA_18_6 — — LOH_19_4 — — CNA_19_5 — — CNA_20_4 — — CNA_21_2 — — CNA_22_3 — — LOH_22_3 — — LOH_22_4 — — CNA_p_16 — — CNA_p_17 — — CNA_p_3 — — CNA_q_1 — — CNA_q_16 — — CNA_q_18 — — LOH_p_10 — — LOH_p_17 — — LOH_p_3 — — CAMTA1 23261 NG_053148; NC_000001 UCK2 7371 NC_000001 RCSD1 92241 NC_000001 LINC01724 105371673 NC_000001 KCNU1 157855 NC_000008 ERLIN2 11160 NC_000008; NG_032059 ASH2L 9070 NC_000008 C8orf86 389649 NC_000008 tmb — — Urinary_Bladder_Urothelial_Carcinoma ARID1A_mut 8289 NC_000001; NG_029965 ASXL2_mut 55252 NG_052995; NC_000002 CSMD1_mut 64478 NC_000008 ELF3_mut 1999 NC_000001 EP300_mut 2033 NG_009817; NC_000022 FAM135B_mut 51059 NC_000008 HMCN1_mut 83872 NC_000001; NG_011841 HUWE1_mut 10075 NC_000023; NG_016261 HYDIN_mut 54768 NC_000016; NG_033116; NW_013171813 IGSF10_mut 285313 NC_000003 KDM6A_mut 7403 NG_016260; NC_000023 KIAA1109_mut 84162 NG_015813; NC_000004 KMT2C_mut 58508 NC_000007; NG_033948 KMT2D_mut 8085 NG_027827; NC_000012 LRP1B_mut 53353 NC_000002; NG_051023 LRRTM4_mut 80059 NC_000002; NG_053082 MACF1_mut 23499 NC_000001; NG_050926 PCLO_mut 27445 NG_047145; NC_000007 RB1_mut 5925 NG_009009; NC_000013 SLC8A1_mut 6546 NC_000002 SPTAN1_mut 6709 NC_000009; NG_027748 STAG2_mut 10735 NC_000023; NG_033796 THSD7A_mut 221981 NC_000007; NG_027670 USH2A_mut 7399 NC_000001; NG_009497 BRAF_p.V600 673 NC_000007; NG_007873 TP53_p.R248 7157 NG_017013; NC_000017 ERBB2_hotspots 2064 NG_007503; NC_000017 NFE2L2_hotspots 4780 NC_000002 FGFR3_hotspots 2261 NC_000004; NG_012632 HRAS_hotspots 3265 NT_187586; NG_007666; NC_000011 ERCC2_hotspots 2068 NC_000019; NG_007067 CNA_1_1 — — CNA_1_4 — — LOH_1_6 — — CNA_1_11 — — CNA_1_15 — — CNA_1_19 — — CNA_1_20 — — CNA_2_6 — — CNA_2_19 — — CNA_2_20 — — LOH_2_22 — — CNA_3_0 — — LOH_3_0 — — CNA_3_1 — — LOH_3_1 — — CNA_3_2 — — LOH_3_2 — — CNA_3_19 — — CNA_4_14 — — CNA_5_1 — — CNA_5_2 — — CNA_5_15 — — CNA_5_16 — — CNA_5_17 — — CNA_6_3 — — CNA_6_4 — — LOH_6_7 — — CNA_6_8 — — CNA_6_9 — — CNA_6_10 — — CNA_6_11 — — CNA_6_12 — — CNA_6_13 — — CNA_6_14 — — CNA_6_16 — — CNA_7_0 — — CNA_7_2 — — CNA_7_4 — — CNA_7_7 — — CNA_7_9 — — CNA_7_10 — — CNA_7_11 — — CNA_7_12 — — CNA_7_13 — — CNA_7_14 — — CNA_7_15 — — CNA_8_0 — — CNA_8_1 — — LOH_8_1 — — CNA_8_2 — — CNA_8_6 — — CNA_9_2 — — LOH_9_2 — — LOH_9_3 — — CNA_9_7 — — LOH_9_7 — — LOH_9_11 — — LOH_9_13 — — CNA_10_0 — — LOH_10_0 — — CNA_10_1 — — CNA_10_4 — — LOH_10_4 — — CNA_10_5 — — CNA_10_11 — — CNA_10_12 — — CNA_11_1 — — LOH_11_1 — — CNA_11_2 — — LOH_11_2 — — CNA_11_3 — — LOH_11_3 — — CNA_11_6 — — CNA_11_7 — — LOH_11_7 — — CNA_11_10 — — CNA_11_11 — — LOH_11_11 — — CNA_11_12 — — CNA_12_7 — — CNA_12_8 — — CNA_12_11 — — LOH_12_11 — — CNA_13_2 — — LOH_13_2 — — CNA_13_3 — — LOH_13_3 — — LOH_13_4 — — CNA_13_5 — — CNA_13_6 — — LOH_13_7 — — CNA_13_8 — — LOH_13_8 — — CNA_13_9 — — LOH_13_10 — — CNA_15_3 — — CNA_15_4 — — LOH_15_6 — — CNA_16_0 — — CNA_16_1 — — LOH_16_4 — — CNA_16_5 — — LOH_16_5 — — CNA_16_6 — — LOH_16_6 — — CNA_16_7 — — LOH_16_7 — — CNA_16_8 — — LOH_16_8 — — CNA_17_0 — — LOH_17_0 — — CNA_17_1 — — LOH_17_1 — — CNA_17_3 — — LOH_17_3 — — LOH_17_4 — — CNA_17_5 — — LOH_17_5 — — CNA_17_6 — — LOH_17_6 — — CNA_17_7 — — LOH_17_7 — — CNA_18_0 — — LOH_18_0 — — CNA_18_2 — — LOH_18_2 — — CNA_18_3 — — LOH_18_7 — — LOH_19_0 — — CNA_19_1 — — CNA_19_4 — — LOH_19_4 — — CNA_19_5 — — CNA_20_3 — — CNA_21_3 — — CNA_22_4 — — LOH_22_4 — — CNA_p_3 — — CNA_q_1 — — CNA_q_13 — — CNA_q_16 — — LOH_p_17 — — LOH_p_3 — — LOH_q_16 — — NYAP2 57624 NC_000002 ASB1 51665 NC_000002 LINC01107 151171 NC_000002 OR6B3 150681 NC_000002 OR5S1P 391496 NG_004369; NC_000002 DUSP28 285193 NC_000002 RNPEPL1 57140 NC_000002 CAPN10 11132 NC_000002; NG_011558 GPR35 2859 NC_000002 ATG4B 23192 NC_000002 DTYMK 1841 NC_000002 D2HGDH 728294 NC_000002; NG_012012 GAL3ST2 64090 NG_046977; NC_000002; NT_187527 LRRIQ4 344657 NC_000003 LRRC31 79782 NC_000003 KRT18P43 151825 NC_000003; NG_009654 SAMD7 344658 NC_000003 SEC62 7095 NC_000003 GPR160 26996 NC_000003 PRKCI 5584 NC_000003 SKIL 6498 NC_000003; NG_030357 SLC7A14 57709 NC_000003; NG_034121 KRT8P13 730023 NG_005969; NG_034121; NC_000003 SLC2A2 6514 NG_008108; NC_000003 TNIK 23043 NG_054934; NC_000003 PLD1 5337 NG_029851; NC_000003 TMEM212-AS1 100874219 NC_000003 TMEM212 389177 NC_000003 RPS27AP8 100271375 NG_010054; NC_000003 TBL1XR1 79718 NC_000003; NG_047195 LINC00501 100820709 NC_000003 ASS1P7 339845 NC_000003; NG_001079 LINC00578 100505566 NC_000003 LINC02015 102724550 NC_000003 KCNMB2 10242 NC_000003 PPIAP75 111082968 NG_065980; NC_000003 ZMAT3 64393 NG_050678; NC_000003 PIK3CA 5290 NC_000003; NG_012113 KCNMB3 27094 NC_000003 LRRFIP1P1 101290506 NC_000003; NG_033175 ACTL6A 86 NC_000003 MRPL47 57129 NC_000003 NDUFB5 4711 NC_000003 USP13 8975 NC_000003 MCCC1 56922 NG_008100; NC_000003 OPA1 4976 NC_000003; NG_011605 LINC02038 105374285 NC_000003 LINC02026 647323 NC_000003 TNK2 10188 NG_029779; NC_000003 fusion_FGFR3_anygene 2261 NC_000004; NG_012632 fusion_FRS2_anygene 10818 NC_000012 fusion_SLC45A3_anygene 85414 NC_000001 fusion_TMPRSS2_anygene 7113 NC_000021; NG_047085 fusion_anygene_C12orf28 196446 NC_000012 fusion_anygene_CPM 1368 NC_000012 fusion_anygene_ERG 2078 NC_000021; NG_029732 fusion_anygene_RET 5979 NG_007489; NC_000010 fusion_anygene_TACC3 10460 NG_064424; NC_000004 fusion_FGFR3_TACC3  2261; 10460 NC_000004; NG_012632 NG_064424; NC_000004 fusion_TMPRSS2_ERG 7113; 2078 NC_000021; NG_047085 NC_000021; NG_029732 tmb — — ploidy — — msi — — Melanoma CNA_12_5 — — LOH_11_10 — — CNA_q_13 — — CNA_3_7 — — BRAF_p.V600 673 NC_000007; NG_007873 CNA_8_0 — — CNA_3_19 — — LOH_17_0 — — LOH_11_9 — — NF1_mut 4763 NC_000017; NG_009018 LOH_3_3 — — CNA_5_2 — — LOH_6_13 — — CNA_22_4 — — LOH_p_10 — — COL4A4_mut 1286 NG_011592; NC_000002 MYH4_mut 4622 NC_000017; NG_052846 CNA_7_12 — — CNA_13_3 — — DESI1 27351 NC_000022 LOH_17_1 — — CNA_9_2 — — CNA_10_2 — — LOH_11_12 — — LOH_9_0 — — CNA_p_16 — — CNA_3_13 — — PIK3CA_hotspots 5290 NC_000003; NG_012113 CSMD2_mut 114784 NC_000001; NG_053181 CNA_10_11 — — CNA_10_7 — — CNA_10_8 — — LOH_18_5 — — POLR3H 171568 NC_000022 LOH_p_3 — — GNAQ_hotspots 2776 NG_027904; NC_000009 CNA_7_14 — — LOH_18_6 — — LOH_3_13 — — TTN_mut 7273 NC_000002; NG_011618 LOH_3_17 — — CNA_3_18 — — MUC16_mut 94025 NC_000019; NG_055257 CNA_10_9 — — KIT_mut 3815 NC_000004; NG_007456 CNA_9_9 — — PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 CNA_6_3 — — CNA_3_16 — — THSD7B_mut 80731 NC_000002 CNA_21_2 — — CNA_p_3 — — CNA_6_4 — — CNA_8_6 — — NRAS_hotspots 4893 NG_007572; NC_000001 GNA11_p.Q209 2767 NC_000019; NG_033852 KCNH5_mut 27133 NG_034062; NC_000014 CNA_13_5 — — PKHD1L1_mut 93035 NC_000008 CNA_7_11 — — CNA_q_1 — — CNA_13_7 — — LOH_11_11 — — WDFY4_mut 57705 NC_000010 CNA_3_6 — — LOH_11_6 — — LOH_p_17 — — CNA_8_1 — — LOH_6_7 — — CNA_q_7 — — CNA_8_5 — — CNA_3_17 — — CSDC2 27254 NC_000022 LOH_10_5 — — LOH_9_1 — — LOH_9_2 — — NRAS_p.Q61 4893 NG_007572; NC_000001 CNA_5_1 — — LOH_3_6 — — COL5A3_mut 50509 NC_000019; NG_046943 CNA_8_2 — — FAM8A1 51439 NC_000006 SHISA8 440829 NC_000022 CNA_8_13 — — msi — — DNAH8_mut 1769 NG_041805; NC_000006 DNAH5_mut 1767 NC_000005; NG_013081 CNA_9_1 — — CNA_11_11 — — LOH_9_6 — — CNA_11_12 — — LOH_9_3 — — CNA_5_14 — — PCDH18_mut 54510 NC_000004 DSCAM_mut 1826 NC_000021 TP53_hotspots 7157 NG_017013; NC_000017 CNA_7_13 — — COL21A1_mut 81578 NC_000006 MGAM_mut 8972 NT_187562; NC_000007; NG_033954 C6_mut 729 NC_000005; NG_011582 LOH_q_9 — — LOH_10_6 — — CNA_10_5 — — CNA_12_8 — — HNRNPA1P37 100421379 NC_000006; NG_033003; NG_025781 NRAS_mut 4893 NG_007572; NC_000001 tmb — — ANK3_mut 288 NG_029917; NC_000010 CNA_7_3 — — DNAH7_mut 56171 NC_000002 CNA_3_11 — — TLL1_mut 7092 NC_000004; NG_016278 CNA_10_12 — — CNA_6_13 — — LOH_11_8 — — GNA11_hotspots 2767 NC_000019; NG_033852 CNA_p_5 — — CNA_13_4 — — CNA_9_3 — — CNA_5_3 — — COL4A3_mut 1285 NC_000002; NG_011591 CNA_3_12 — — LOH_6_12 — — CNA_6_1 — — LOH_15_4 — — CNA_12_1 — — LOH_19_0 — — GNAQ_p.Q209 2776 NG_027904; NC_000009 LOH_3_7 — — CNA_9_0 — — CNA_18_6 — — CNA_1_15 — — LOH_10_2 — — CNA_6_0 — — PCLO_mut 27445 NG_047145; NC_000007 BRAF_hotspots 673 NC_000007; NG_007873 LOH_8_0 — — CNTN5_mut 53942 NC_000011; NG_047156 CNA_p_10 — — CNA_q_22 — — ARID1A_mut 8289 NC_000001; NG_029965 CNA_6_2 — — CNA_10_6 — — Thyroid_Neoplasm CNA_2_5 — — CNA_20_5 — — CNA_q_13 — — BRAF_p.V600 673 NC_000007; NG_007873 CNA_8_12 — — LOH_17_0 — — HRAS_p.Q61 3265 NT_187586; NG_007666; NC_000011 LOH_11_2 — — ZFHX4_mut 79776 NC_000008 CNA_22_4 — — LOH_1_9 — — LOH_22_2 — — CNA_10_2 — — LOH_9_0 — — LOH_q_22 — — CNA_p_16 — — PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_1_0 — — CNA_10_11 — — CNA_14_8 — — LOH_5_11 — — LOH_p_3 — — CNA_7_14 — — LOH_8_1 — — TTN_mut 7273 NC_000002; NG_011618 CNA_16_7 — — MUC16_mut 94025 NC_000019; NG_055257 CNA_q_18 — — CNA_6_16 — — CNA_1_14 — — CNA_6_3 — — CNA_p_7 — — IDH1_p.R132 3417 NG_023319; NC_000002 CNA_p_3 — — LOH_1_0 — — NRAS_hotspots 4893 NG_007572; NC_000001 LOH_19_1 — — CNA_q_1 — — CNA_5_11 — — IDH1_hotspots 3417 NG_023319; NC_000002 CNA_17_3 — — LOH_p_17 — — CNA_q_7 — — CNA_8_1 — — LOH_17_4 — — LOH_6_7 — — LOH_1_2 — — CNA_3_17 — — CNA_16_8 — — CNA_q_16 — — CNA_2_8 — — NRAS_p.Q61 4893 NG_007572; NC_000001 LOH_22_3 — — CNA_8_13 — — msi — — CNA_2_2 — — TRGV3 6976 NC_000007; NG_001336 TP53_hotspots 7157 NG_017013; NC_000017 CNA_7_0 — — CNA_q_8 — — NRAS_mut 4893 NG_007572; NC_000001 tmb — — CNA_7_3 — — RYR3_mut 6263 NC_000015; NG_047076 LOH_1_8 — — LOH_q_16 — — CNA_10_12 — — HRAS_hotspots 3265 NT_187586; NG_007666; NC_000011 FAT4_mut 79633 NG_033865; NC_000004 CNA_p_5 — — CNA_22_2 — — CNA_q_20 — — LOH_3_2 — — LOH_19_0 — — GTF2I_p.L424 2969 NC_000007; NG_008179 CNA_6_0 — — LOH_14_9 — — CNA_22_3 — — BRAF_hotspots 673 NC_000007; NG_007873 LOH_8_0 — — LOH_6_16 — — CNA_q_22 — — ARID1A_mut 8289 NC_000001; NG_029965 TRGV8 6982 NC_000007; NG_001336 CNA_2_18 — — CNA_6_2 — — LOH_6_9 — — Sarcoma CDH9_mut 1007 NC_000005; NG_046968 CDKN2A_mut 1029 NC_000009; NG_007485 CHD8_mut 57680 NC_000014; NG_021249 CHD9_mut 80205 NC_000016 CHL1_mut 1663 NC_000012; NG_023352 CHRM2_mut 1129 NC_000007; NG_011846 CHRM3_mut 1131 NC_000001; NG_032046 CIC_mut 23152 NC_000019; NG_042060 CMYA5_mut 202333 NC_000005 CNOT1_mut 23019 NC_000016 CNTN3_mut 5067 NC_000003 CNTNAP5_mut 129684 NC_000002 CNTRL_mut 11064 NC_000009 COL11A1_mut 1301 NC_000001; NG_008033 COL11A2_mut 1302 NG_011589; NT_167249; NT_167246; NC_000006; NT_167247; NT_113891; NT_167248; NT_167245 COL21A1_mut 81578 NC_000006 COL22A1_mut 169044 NG_054761; NC_000008 COL24A1_mut 255631 NC_000001; NG_053093 COL28A1_mut 340267 NC_000007 COL2A1_mut 1280 NG_008072; NC_000012 COL3A1_mut 1281 NG_007404; NC_000002 COL4A1_mut 1282 NC_000013; NG_011544 COL4A2_mut 1284 NG_032137; NC_000013 COL4A3_mut 1285 NC_000002; NG_011591 COL4A4_mut 1286 NG_011592; NC_000002 COL4A5_mut 1287 NC_000023; NG_011977 COL4A6_mut 1288 NG_012059; NC_000023 COL5A1_mut 1289 NG_008030; NC_000009 COL5A2_mut 1290 NC_000002; NG_011799 COL5A3_mut 50509 NC_000019; NG_046943 COL6A3_mut 1293 NG_008676; NC_000002 COL6A6_mut 131873 NC_000003; NG_054914 COL7A1_mut 1294 NC_000003; NG_007065 COL8A1_mut 1295 NC_000003 COL9A1_mut 1297 NC_000006; NG_011654 COPA_mut 1314 NG_050927; NC_000001 CORIN_mut 10699 NG_032679; NC_000004 CPAMD8_mut 27151 NG_054892; NC_000019 CPED1_mut 79974 NC_000007 CPS1_mut 1373 NC_000002; NG_008285 CRB1_mut 23418 NG_008483; NC_000001 CREBBP_mut 1387 NG_009873; NC_000016 CSMD1_mut 64478 NC_000008 CSMD2_mut 114784 NC_000001; NG_053181 CSMD3_mut 114788 NC_000008 CTCF_mut 10664 NC_000016; NG_033892 CTNNA2_mut 1496 NC_000002 CTNNA3_mut 29119 NG_034072; NC_000010 CTNNB1_mut 1499 NC_000003; NG_013302 CTNND2_mut 1501 NC_000005; NG_023544 CUBN_mut 8029 NC_000010; NG_008967 CUL9_mut 23113 NC_000006 DCAF4L2_mut 138009 NC_000008 DCC_mut 1630 NC_000018; NG_013341 DCDC1_mut 341019 NC_000011 DCHS1_mut 8642 NC_000011; NG_033858 DDX60_mut 55601 NG_054636; NC_000004 DDX60L_mut 91351 NC_000004; NG_051576 DENND5B_mut 160518 NC_000012 DMD_mut 1756 NC_000023; NG_012232 DNAH3_mut 55567 NC_000016; NG_052617 DNAH7_mut 56171 NC_000002 DNAH9_mut 1770 NG_047047; NC_000017 IDH1_p.R132 3417 NG_023319; NC_000002 BRAF_hotspots 673 NC_000007; NG_007873 IDH1_hotspots 3417 NG_023319; NC_000002 CNA_1_1 — — CNA_1_2 — — CNA_1_5 — — CNA_1_6 — — CNA_1_18 — — CNA_1_19 — — CNA_1_20 — — LOH_1_20 — — CNA_1_21 — — CNA_1_22 — — CNA_1_23 — — CNA_1_24 — — CNA_2_1 — — LOH_2_1 — — CNA_2_2 — — CNA_2_4 — — CNA_2_5 — — LOH_2_6 — — CNA_2_8 — — CNA_3_0 — — LOH_3_0 — — CNA_3_1 — — CNA_3_2 — — CNA_3_3 — — LOH_3_7 — — CNA_3_8 — — CNA_3_15 — — CNA_3_16 — — CNA_3_17 — — LOH_3_17 — — CNA_3_19 — — CNA_4_2 — — CNA_4_3 — — CNA_4_10 — — CNA_4_11 — — LOH_4_11 — — CNA_4_14 — — CNA_4_16 — — LOH_4_17 — — CNA_5_0 — — CNA_5_1 — — CNA_5_2 — — CNA_5_3 — — LOH_5_3 — — CNA_5_6 — — LOH_5_6 — — LOH_5_7 — — LOH_5_9 — — LOH_5_10 — — LOH_5_12 — — CNA_6_0 — — CNA_6_1 — — CNA_6_2 — — CNA_6_3 — — LOH_6_4 — — CNA_6_10 — — LOH_6_10 — — CNA_6_11 — — LOH_6_13 — — CNA_6_15 — — LOH_6_15 — — CNA_6_16 — — CNA_7_0 — — CNA_7_5 — — CNA_7_12 — — CNA_7_14 — — CNA_7_15 — — CNA_8_0 — — CNA_8_1 — — CNA_8_2 — — CNA_8_9 — — CNA_8_11 — — CNA_8_12 — — CNA_8_13 — — CNA_9_2 — — CNA_9_3 — — LOH_9_3 — — CNA_9_7 — — LOH_10_0 — — LOH_10_5 — — CNA_10_6 — — LOH_10_6 — — LOH_10_7 — — CNA_11_6 — — CNA_12_1 — — CNA_12_5 — — CNA_12_6 — — CNA_12_7 — — CNA_12_8 — — CNA_13_2 — — LOH_13_2 — — CNA_13_4 — — LOH_13_4 — — CNA_13_5 — — CNA_15_5 — — LOH_16_4 — — LOH_16_7 — — LOH_16_8 — — CNA_17_0 — — LOH_17_0 — — CNA_17_1 — — LOH_17_1 — — CNA_17_3 — — CNA_18_3 — — CNA_18_5 — — CNA_18_6 — — LOH_18_6 — — CNA_18_7 — — CNA_19_0 — — LOH_19_0 — — CNA_19_3 — — CNA_19_4 — — CNA_19_5 — — CNA_20_0 — — CNA_20_1 — — CNA_20_5 — — CNA_22_4 — — CNA_p_10 — — CNA_p_16 — — CNA_p_17 — — CNA_p_18 — — CNA_p_20 — — CNA_p_3 — — CNA_p_5 — — CNA_q_1 — — CNA_q_13 — — CNA_q_18 — — CNA_q_20 — — CNA_q_7 — — LOH_p_17 — — LOH_p_3 — — CEP57L1P1 221017 NG_005976; NC_000010 PRF1 5551 NC_000010; NG_009615 CFAP70 118491 NC_000010 TIMM9P1 100862726 NG_032133; NC_000010; NG_030484 COMTD1 118881 NC_000010 RPL39P25 100271517 NC_000010; NG_010826 HMGA1P5 387063 NC_000010; NG_008009 SPA17P1 171424 NG_001328; NC_000010 ZNF503 84858 NC_000010 LRMDA 83938 NC_000010; NG_042180 ATP5MC1P8 100288222 NC_000010; NG_028756 KCNMA1 3778 NC_000010; NG_012270 COX6CP15 106480268 NC_000010; NG_045680; NG_012270 IMPDH1P5 340780 NG_005147; NT_187580; NC_000010 DLG5 9231 NC_000010; NG_011484; NT_187580 POLR3A 11128 NG_029648; NC_000010 RPS24 6229 NC_000010; NG_012633 GNAI2P2 401646 NC_000010; NG_030117 ZMIZ1 57178 NC_000010; NG_028289 RPS12P18 100271354 NC_000010; NG_011294 SFTPA2 729238 NG_013046; NC_000010 MBL3P 50639 NC_000010; NG_029674 SFTPA3P 100288405 NG_016155; NC_000010 SFTPA1 653509 NG_021189; NC_000010 BEND3P3 650623 NG_011922; NC_000010 NUTM2B 729262 NG_012780; NC_000010 RPL22P18 100271290 NG_010959; NC_000010 PLAC9 219348 NC_000010 ANXA11 311 NC_000010 LINC00857 439990 NC_000010 RPS12P2 619448 NG_009566; NC_000010 EIF5AP4 642592 NC_000010; NG_006529 DYDC2 84332 NC_000010 PRXL2A 84293 NC_000010 TSPAN14 81619 NC_000010 SH2D4B 387694 NC_000010 RPS7P9 100128756 NC_000010; NG_011267 FARSBP1 647532 NC_000010; NG_005861 WARS2P1 100421633 NG_025451; NC_000010 RPA2P2 389990 NC_000010; NG_022150 NRG3 10718 NG_013373; NC_000010 MARK2P15 100533794 NC_000010; NG_028751 CACYBPP1 100420043 NG_025452; NC_000010 TUBGCP2 10844 NC_000010 ZNF511 118472 NC_000010 ZNF511-PRAP1 104326056 NC_000010 CALY 50632 NC_000010 BANF1P2 414169 NG_029687; NC_000010 ANKRD26P1 124149 NC_000016 SHCBP1 79801 NC_000016 RAB43P1 440375 NC_000016; NG_005358 VPS35 55737 NC_000016; NG_029970 ORC6 23594 NG_028241; NC_000016 NETO2 81831 NC_000016; NG_047201 LINC02133 101927132 NC_000016 LINC01571 101927364 NC_000016 LINC00919 100505619 NC_000016 LINC02180 102467079 NC_000016 CASC22 283854 NC_000016 TOX3 27324 NC_000016; NG_012623 CASC16 643714 NC_000016 PHBP21 390730 NG_022521; NC_000016 CHD9 80205 NC_000016 RBL2 5934 NC_000016 AKTIP 64400 NC_000016 RPL13P12 388344 NC_000017; NG_007541 TSEN15P1 100288179 NG_030099; NC_000017 MED9 55090 NC_000017 RASD1 51655 NG_028074; NC_000017 PEMT 10400 NC_000017 SMCR2 105371564 NC_000017 RAI1 10743 NC_000017; NG_007101 SREBF1 6720 NC_000017; NG_029029 TOM1L2 146691 NC_000017; NG_053113 DRC3 83450 NC_000017 ATPAF2 91647 NC_000017; NG_012824 GID4 79018 NC_000017 DRG2 1819 NC_000017 MYO15A 51168 NC_000017; NG_011634 ALKBH5 54890 NC_000017 LLGL1 3996 NC_000017; NW_017363819 FLII 2314 NC_000017; NW_017363819; NG_023243 MIEF2 125170 NW_017363819; NC_000017 TOP3A 7156 NW_017363819; NC_000017 CCDC144B 284047 NC_000017 ZNF286B 729288 NC_000017 FOXO3B 2310 NC_000017; NG_001119 UBE2SP2 440406 NG_031882; NC_000017 TRIM16L 147166 NC_000017 B3GNT7 93010 NC_000002 ZBTB8OSP2 729898 NG_028934; NC_000002 NCL 4691 NC_000002 LINC00471 151477 NC_000002 NMUR1 10316 NC_000002 RPE23AP26 391490 NG_010355; NC_000002 TEX44 165100 NC_000002 PTMA 5757 NC_000002 PDE6D 5147 NG_034064; NC_000002 COPS7B 64708 NC_000002 NPPC 4880 NC_000002 ECEL1P3 260332 NG_002700; NC_000002 ALPP 250 NG_012189; NC_000002 ECEL1P2 347694 NG_023671; NC_000002; NG_002701 ALPG 251 NC_000002 ALPI 248 NC_000002 ECEL1 9427 NG_034065; NC_000002 AGAP1 116987 NG_030314; NC_000002 TMSB10P1 100506723 NG_030314; NC_000002; NG_029007 GBX2 2637 NC_000002 ASB18 401036 NG_053045; NC_000002 IQCA1 79781 NC_000002 RPL3P5 100130450 NC_000002; NG_010767 ACKR3 57007 NC_000002 HDAC4 9759 NC_000002; NG_009235 OR6B3 150681 NC_000002 OR9S24P 403275 NC_000002; NG_005821 OR5S1P 391496 NG_004369; NC_000002 COPS9 150678 NC_000002 OTOS 150677 NC_000002 GPC1 2817 NC_000002 ANKMY1 51281 NC_000002 DUSP28 285193 NC_000002 RNPEPL1 57140 NC_000002 CAPN10 11132 NC_000002; NG_011558 GPR35 2859 NC_000002 AQP12B 653437 NC_000002 AQP12A 375318 NC_000002 KIF1A 547 NG_029724; NC_000002 AGXT 189 NC_000002; NG_008005 MAB21L4 79919 NC_000002 CROCC2 728763 NC_000002 SNED1 25992 NC_000002 MTERF4 130916 NC_000002 PASK 23178 NG_052850; NC_000002 PPP1R7 5510 NC_000002 ANO7 50636 NC_000002; NG_029845 HDLBP 3069 NC_000002 FARP2 9855 NC_000002 STK25 10494 NC_000002 BOK 666 NC_000002; NG_029488 THAP4 51078 NC_000002 ATG4B 23192 NC_000002 DTYMK 1841 NC_000002 ING5 84289 NC_000002 D2HGDH 728294 NC_000002; NG_012012 GAL3ST2 64090 NG_046977; NC_000002; NT_187527 NEU4 129807 NT_187527; NC_000002 PDCD1 5133 NC_000002; NG_012110; NT_187527 RTP5 285093 NT_187527; NC_000002 LINC01880 105373979 NC_000002; NT_187647; NT_187523 RPL23AP88 100289034 NG_030145; NC_000002 fusion_FGFR3_anygene 2261 NC_000004; NG_012632 fusion_FRS2_anygene 10818 NC_000012 fusion_SLC45A3_anygene 85414 NC_000001 fusion_TMPRSS2_anygene 7113 NC_000021; NG_047085 fusion_anygene_C12orf28 196446 NC_000012 fusion_anygene_CPM 1368 NC_000012 fusion_anygene_ERG 2078 NC_000021; NG_029732 fusion_anygene_NUP107 57122 NG_046600; NC_000012 fusion_anygene_RET 5979 NG_007489; NC_000010 fusion_anygene_TACC3 10460 NG_064424; NC_000004 fusion_FGFR3_TACC3  2261; 10460 NC_000004; NG_012632 NG_064424; NC_000004 fusion_TMPRSS2_ERG 7113; 2078 NC_000021; NG_047085 NC_000021; NG_029732 tmb — — Uterus_Carcinoma PTEN_hotspots 5728 NC_000010; NW_013171807; NG_007466 LOH_9_11 — — PIK3CA_mut 5290 NC_000003; NG_012113 CNA_8_12 — — CNA_7_7 — — LOH_17_0 — — CTNNB1_mut 1499 NC_000003; NG_013302 LOH_17_1 — — CNA_9_2 — — CNA_4_18 — — LOH_22_2 — — LOH_9_0 — — CNA_p_16 — — CNA_3_13 — — LOH_16_7 — — CNA_19_3 — — PREX2_mut 80243 NG_047022; NC_000008 PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_6_9 — — CNA_10_7 — — CNA_10_8 — — LOH_p_3 — — LOH_1_4 — — LOH_8_1 — — CNA_16_7 — — PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 FBXW7_hotspots 55294 NC_000004; NG_029466 LOH_1_11 — — CNA_9_9 — — LOH_8_2 — — CNA_3_16 — — CNA_5_17 — — LOH_16_4 — — CNA_p_3 — — CNA_7_8 — — LOH_11_3 — — CNA_1_18 — — CNA_q_1 — — CTCF_mut 10664 NC_000016; NG_033892 CNA_6_12 — — CNA_7_5 — — CNA_5_11 — — CNA_3_6 — — CNA_3_4 — — LOH_6_7 — — CNA_q_7 — — CNA_q_16 — — LOH_9_2 — — LOH_3_6 — — PIK3R1_mut 5295 NC_000005; NG_012849 CNA_8_2 — — CNA_8_13 — — msi — — CNA_10_4 — — LOH_9_8 — — CNA_1_22 — — LOH_11_6 — — CNA_2_2 — — LOH_9_9 — — CNA_5_13 — — CNA_19_0 — — CNA_6_15 — — CNA_q_8 — — CNA_1_4 — — tmb — — KRAS_hotspots 3845 NC_000012; NG_007524 CNA_7_3 — — CNA_3_11 — — CNA_4_17 — — CNA_20_1 — — CNA_15_4 — — CNA_17_1 — — LOH_5_17 — — LOH_11_8 — — TAF1_mut 6872 NC_000023; NG_012771 CNA_2_6 — — CNA_p_5 — — CNA_16_2 — — MED12_mut 9968 NG_012808; NC_000023 LOH_10_12 — — CNA_9_10 — — CNA_9_11 — — CNA_9_12 — — LOH_19_0 — — CNA_1_15 — — CNA_4_1 — — CNA_16_6 — — CNA_6_0 — — CNA_17_7 — — CNA_1_3 — — CNA_9_13 — — CNA_2_1 — — LOH_16_8 — — BRAF_hotspots 673 NC_000007; NG_007873 CNA_11_3 — — CNA_19_5 — — CTNNB1_hotspots 1499 NC_000003; NG_013302 ARID1A_mut 8289 NC_000001; NG_029965 LOH_11_1 — — CNA_17_0 — — CNA_q_22 — — CNA_10_6 — — Glioma ATRX_mut 546 NC_000023; NG_008838 CELSR1_mut 9620 NG_030466; NC_000022 PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 TTN_mut 7273 NC_000002; NG_011618 IDH1_p.R132 3417 NG_023319; NC_000002 TP53_hotspots 7157 NG_017013; NC_000017 EGFR_hotspots 1956 NG_007726; NC_000007 BRAF_hotspots 673 NC_000007; NG_007873 IDH1_hotspots 3417 NG_023319; NC_000002 CNA_1_0 — — CNA_1_1 — — CNA_1_2 — — CNA_1_10 — — CNA_1_11 — — CNA_1_14 — — LOH_3_0 — — CNA_3_6 — — LOH_3_6 — — CNA_3_16 — — CNA_4_1 — — CNA_4_2 — — CNA_4_9 — — CNA_5_2 — — CNA_5_3 — — LOH_5_6 — — LOH_5_11 — — CNA_6_0 — — CNA_6_1 — — CNA_6_2 — — CNA_6_4 — — CNA_7_5 — — CNA_7_7 — — CNA_7_11 — — CNA_7_13 — — CNA_7_14 — — CNA_7_15 — — CNA_8_0 — — CNA_8_1 — — LOH_8_1 — — CNA_8_2 — — LOH_8_2 — — CNA_8_8 — — CNA_8_9 — — CNA_8_10 — — CNA_8_12 — — CNA_9_2 — — LOH_9_2 — — LOH_9_7 — — CNA_9_8 — — CNA_10_1 — — CNA_10_2 — — LOH_10_2 — — CNA_10_4 — — LOH_10_4 — — CNA_10_5 — — LOH_10_5 — — CNA_10_6 — — CNA_10_7 — — CNA_10_12 — — CNA_11_2 — — CNA_11_11 — — CNA_11_12 — — CNA_12_2 — — CNA_12_10 — — LOH_14_7 — — LOH_14_8 — — CNA_16_0 — — CNA_16_1 — — CNA_16_2 — — CNA_16_4 — — CNA_17_0 — — CNA_17_1 — — CNA_17_3 — — LOH_18_5 — — CNA_18_7 — — CNA_19_0 — — CNA_19_3 — — LOH_19_3 — — LOH_19_4 — — CNA_19_5 — — LOH_19_5 — — CNA_20_4 — — CNA_21_3 — — CNA_p_10 — — CNA_p_16 — — CNA_p_17 — — CNA_p_3 — — CNA_p_5 — — CNA_p_7 — — CNA_q_1 — — CNA_q_13 — — CNA_q_16 — — CNA_q_18 — — CNA_q_20 — — CNA_q_7 — — CNA_q_8 — — LOH_p_10 — — LOH_p_17 — — LOH_p_3 — — LOH_q_16 — — NLRP13 126204 NG_053013; NC_000019 LINC01864 101928886 NC_000019 ZNF542P 147947 NC_000019 ZNF264 9422 NG_016432; NC_000019 TSGA13 114960 NC_000007 ZC3HAV1L 92092 NC_000007 tmb — — ploidy — — Renal_Cell_Carcinoma CNA_12_5 — — CNA_16_4 — — CNA_q_13 — — CNA_12_12 — — LOH_5_10 — — BRAF_p.V600 673 NC_000007; NG_007873 LOH_1_21 — — CNA_5_9 — — CNA_8_0 — — CNA_19_1 — — CNA_3_19 — — CNA_20_0 — — CNA_8_12 — — CNA_7_7 — — CNA_5_7 — — LOH_17_0 — — KCTD16 57528 NC_000005 LOH_2_18 — — CNA_21_3 — — LOH_3_3 — — CNA_5_2 — — CNA_22_4 — — CNA_18_4 — — LOH_p_10 — — APC_mut 324 NG_008481; NC_000005 CNA_7_12 — — CNA_9_2 — — LOH_11_12 — — LOH_q_22 — — LOH_16_7 — — CNA_19_3 — — CNA_16_0 — — PIK3CA_hotspots 5290 NC_000003; NG_012113 LOH_2_6 — — LOH_14_4 — — CNA_1_0 — — CNA_10_11 — — CNA_10_7 — — CNA_10_8 — — LOH_5_11 — — LOH_p_3 — — LOH_1_20 — — LOH_1_17 — — LOH_1_4 — — CNA_7_14 — — LOH_13_2 — — LOH_8_1 — — LOH_19_4 — — LOH_3_13 — — CNA_16_7 — — CNA_3_10 — — CNA_10_9 — — CNA_5_10 — — PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 CNA_9_9 — — CNA_1_20 — — CNA_q_18 — — CNA_1_14 — — CNA_5_16 — — CNA_6_3 — — CNA_p_7 — — CNA_21_2 — — CNA_5_17 — — IDH1_p.R132 3417 NG_023319; NC_000002 CNA_8_11 — — CNA_p_3 — — LOH_13_3 — — CNA_6_4 — — LOH_12_12 — — CNA_8_8 — — CNA_1_18 — — LOH_19_1 — — CNA_q_1 — — CNA_13_7 — — CNA_1_24 — — LOH_6_10 — — IDH1_hotspots 3417 NG_023319; NC_000002 CNA_17_3 — — CNA_12_10 — — VHL_mut 7428 NC_000003; NG_008212 LOH_11_6 — — LOH_10_9 — — LOH_p_17 — — LOH_6_7 — — LOH_17_4 — — CNA_q_7 — — CNA_8_1 — — CNA_8_7 — — ploidy — — CNA_8_5 — — CNA_12_2 — — PBRM1_mut 55193 NG_032108; NC_000003 LOH_1_2 — — CNA_3_17 — — CNA_10_0 — — CNA_16_8 — — KRAS_mut 3845 NC_000012; NG_007524 CNA_q_16 — — LOH_9_1 — — LOH_14_3 — — LOH_9_2 — — CNA_5_1 — — CNA_12_11 — — CNA_12_6 — — CNA_8_2 — — CNA_15_5 — — LOH_3_0 — — LOH_12_8 — — CNA_10_4 — — CNA_5_8 — — LOH_9_12 — — LOH_1_6 — — CNA_11_12 — — CNA_3_2 — — CNA_5_14 — — CNA_1_19 — — CNA_p_17 — — TP53_hotspots 7157 NG_017013; NC_000017 LOH_3_5 — — HMGB1P5 10354 NG_000897; NC_000003 CNA_5_15 — — LOH_q_9 — — CNA_2_23 — — CNA_1_2 — — CNA_3_0 — — LOH_2_19 — — CNA_q_8 — — LOH_18_2 — — LOH_3_1 — — CNA_9_8 — — tmb — — KRAS_hotspots 3845 NC_000012; NG_007524 LOH_19_5 — — CNA_3_11 — — LOH_1_3 — — LOH_1_8 — — LOH_12_11 — — LOH_1_13 — — CNA_6_10 — — LOH_q_16 — — CNA_17_1 — — CNA_17_5 — — LOH_10_11 — — LOH_5_17 — — CNA_2_17 — — LOH_3_4 — — CNA_5_12 — — CNA_p_5 — — CNA_2_6 — — CNA_9_7 — — CNA_22_2 — — CNA_9_3 — — CNA_12_7 — — CNA_3_12 — — CNA_q_20 — — CNA_6_1 — — LOH_3_2 — — LOH_10_8 — — CNA_12_1 — — CNA_16_5 — — CNA_19_4 — — CNA_9_12 — — CNA_1_15 — — CNA_11_6 — — CNA_16_6 — — CNA_6_0 — — CNA_17_7 — — CNA_1_3 — — LOH_4_17 — — CNA_9_13 — — LOH_1_18 — — BRAF_hotspots 673 NC_000007; NG_007873 CNA_1_21 — — LOH_8_0 — — LOH_6_16 — — LOH_1_1 — — CNA_19_5 — — SETD2_mut 29072 NC_000003; NG_032091 CNA_p_10 — — CNA_q_22 — — CTNNB1_hotspots 1499 NC_000003; NG_013302 ARID1A_mut 8289 NC_000001; NG_029965 CNA_2_13 — — CNA_17_0 — — CNA_6_2 — — CNA_3_14 — — Germ_Cell_Neoplasm ADAMTS18_mut 170692 NG_031879; NC_000016 ADAMTS19_mut 171019 NC_000005 ADAMTS2_mut 9509 NW_016107298; NC_000005; NG_023212 ADAMTS20_mut 80070 NC_000012; NG_028228 ADAMTS3_mut 9508 NC_000004; NG_046955 ADAMTS9_mut 56999 NC_000003 ADAMTSL3_mut 57188 NC_000015 ADCY1_mut 107 NG_034198; NC_000007 ADCY10_mut 55811 NG_016139; NC_000001 ADCY2_mut 108 NG_046913; NC_000005 ADCY5_mut 111 NC_000003; NG_033882 ADGRV1_mut 84059 NG_007083; NC_000005 AFF2_mut 2334 NC_000023; NG_016313 AHCTF1_mut 25909 NC_000001 AHNAK_mut 79026 NC_000011; NG_051822 AHNAK2_mut 113146 NC_000014; NG_054630 AK9_mut 221264 NC_000006 AKAP6_mut 9472 NC_000014 AKAP9_mut 10142 NC_000007; NG_011623 ALPK2_mut 115701 NC_000018 KIT_mut 3815 NC_000004; NG_007456 TP53_hotspots 7157 NG_017013; NC_000017 KIT_hotspots 3815 NC_000004; NG_007456 CNA_2_13 — — CNA_2_23 — — CNA_3_5 — — CNA_3_19 — — CNA_5_0 — — CNA_5_1 — — CNA_5_2 — — CNA_5_3 — — CNA_7_0 — — CNA_7_3 — — CNA_7_7 — — CNA_7_14 — — CNA_8_0 — — CNA_8_1 — — CNA_8_2 — — CNA_8_8 — — CNA_11_6 — — CNA_11_10 — — CNA_12_1 — — CNA_13_10 — — CNA_14_8 — — CNA_14_9 — — LOH_16_7 — — LOH_17_0 — — CNA_18_0 — — CNA_18_2 — — CNA_20_3 — — CNA_21_2 — — CNA_21_3 — — CNA_22_2 — — CNA_p_16 — — CNA_p_17 — — CNA_p_20 — — CNA_p_5 — — CNA_p_7 — — CNA_q_20 — — CNA_q_7 — — LOH_p_10 — — LOH_p_17 — — KDM2A 22992 NC_000011 GRK2 156 NC_000011 DEFB109F 110806268 NC_000012; NG_065970 AICDA 57379 NC_000012; NG_011588 M6PR 4074 NC_000012 PRB1 5542 NT_187658; NC_000012 PRB2 653247 NC_000012; NT_187588 HIGD1AP8 100874451 NC_000012; NG_032545 STX8P1 100423046 NG_021191; NC_000012 RPL13AP20 387841 NC_000012 HTR7P1 93164 NC_000012 EGLN3P1 100420503 NG_023986; NC_000012 SLCO1B3 28234 NG_032071; NC_000012 GOLT1B 51026 NC_000012 SPX 80763 NC_000012 KCNJ8 3764 NG_041794; NC_000012 ABCC9 10060 NC_000012; NG_012819 CMAS 55907 NC_000012 ETNK1 55500 NC_000012; NG_065161 SOX5 6660 NC_000012; NG_029612 ITPR2 3709 NG_042142; NC_000012 INTS13 55726 NC_000012 FGFR1OP2 26127 NC_000012 TM7SF3 51768 NC_000012 MED21 9412 NC_000012 STK38L 23012 NC_000012 ARNTL2 56938 NG_030359; NC_000012 SMCO2 341346 NC_000012 PPFIBP1 8496 NC_000012 REP15 387849 NC_000012 HMGB1P49 100420013 NC_000012; NG_024073 MRPS35 60488 NC_000012 MANSC4 100287284 NC_000012 KLHL42 57542 NC_000012 PTHLH 5744 NC_000012; NG_023197 CCDC91 55297 NC_000012 FAR2 55711 NC_000012 ERGIC2 51290 NC_000012 OVCH1 341350 NC_000012 TMTC1 83857 NC_000012 RPL21P99 100271429 NC_000012; NG_010366 LINC02386 105369717 NC_000012 IPO8 10526 NC_000012 CAPRIN2 65981 NG_029557; NC_000012 LINC00941 100287314 NC_000012 PPIAP44 111082964 NC_000012; NG_065976 TSPAN11 441631 NC_000012 DDX11 1663 NC_000012; NG_023352 DENND5B 160518 NC_000012 fusion_FRS2_anygene 10818 NC_000012 fusion_TMPRSS2_anygene 7113 NC_000021; NG_047085 fusion_anygene_ERG 2078 NC_000021; NG_029732 fusion_TMPRSS2_ERG 7113; 2078 NC_000021; NG_047085 NC_000021; NG_029732 tmb — — Thymoma CNA_12_12 — — CNA_4_11 — — CNA_3_7 — — LOH_6_4 — — CNA_5_9 — — CNA_8_0 — — LOH_13_5 — — CNA_22_4 — — LOH_p_10 — — LOH_17_1 — — CNA_4_18 — — CNA_p_16 — — LOH_14_4 — — CNA_14_8 — — LOH_p_3 — — CNA_7_14 — — CNA_16_7 — — GTF2I_hotspots 2969 NC_000007; NG_008179 CNA_4_9 — — CNA_q_18 — — CNA_1_14 — — EOH_8_2 — — CNA_6_3 — — CNA_p_7 — — CNA_3_16 — — LOH_14_8 — — EOH_16_4 — — CNA_p_3 — — CNA_6_4 — — CNA_1_1 — — CNA_13_5 — — LOH_6_1 — — CNA_q_1 — — CNA_13_9 — — CNA_1_24 — — CNA_17_3 — — LOH_p_17 — — CNA_q_7 — — CNA_8_1 — — CNA_7_4 — — CNA_16_8 — — CNA_q_16 — — LOH_9_1 — — LOH_9_2 — — CNA_8_2 — — LOH_3_0 — — CNA_9_1 — — CNA_10_4 — — CNA_1_19 — — CNA_p_17 — — TRGV3 6976 NC_000007; NG_001336 CNA_7_0 — — CNA_1_2 — — CNA_q_8 — — tmb — — CNA_7_3 — — CNA_1_23 — — LOH_q_16 — — CNA_17_1 — — CNA_15_4 — — CNA_22_2 — — CNA_9_3 — — CNA_12_7 — — CNA_6_1 — — CNA_9_0 — — CNA_1_15 — — CNA_4_1 — — GTF2I_p.L424 2969 NC_000007; NG_008179 CNA_6_0 — — CNA_11_10 — — CNA_5_0 — — LOH_16_8 — — CNA_p_10 — — TRGV8 6982 NC_000007; NG_001336 LOH_16_6 — — CNA_6_2 — — CNA_10_6 — — Pheochromocytoma CNA_1_17 — — CNA_3_1 — — CNA_11_9 — — CNA_q_13 — — CNA_12_12 — — CNA_3_7 — — CNA_5_9 — — CNA_8_0 — — CNA_1_5 — — CNA_3_19 — — CNA_8_12 — — CNA_4_14 — — LOH_11_2 — — LOH_16_5 — — CNA_22_4 — — CNA_p_18 — — LOH_1_9 — — LOH_22_4 — — CNA_17_6 — — CNA_9_2 — — LOH_3_18 — — CNA_p_16 — — CNA_3_13 — — CNA_19_3 — — LOH_3_14 — — LOH_14_4 — — CNA_1_0 — — CNA_10_8 — — CNA_1_9 — — CNA_8_9 — — LOH_18_5 — — CDK4P1 359941 NG_006109; NC_000001 LOH_p_3 — — LOH_1_4 — — LOH_18_6 — — LOH_3_13 — — LOH_19_4 — — CNA_8_10 — — CNA_3_18 — — LOH_3_17 — — CNA_1_6 — — CNA_3_10 — — CNA_10_9 — — LOH_1_11 — — CNA_9_9 — — CNA_1_10 — — CNA_q_18 — — CNA_1_14 — — CNA_5_16 — — CNA_6_3 — — CNA_3_16 — — CNA_21_2 — — CNA_p_7 — — LOH_14_8 — — IDH1_p.R132 3417 NG_023319; NC_000002 CNA_8_11 — — CNA_p_3 — — CNA_1_1 — — LOH_1_0 — — LOH_11_3 — — CNA_8_6 — — LMO4 8543 NC_000001 CNA_13_5 — — CNA_8_8 — — LOH_14_5 — — CNA_q_1 — — CNA_1_24 — — CNA_17_3 — — CNA_3_6 — — LOH_11_6 — — LOH_11_7 — — LOH_17_4 — — LOH_3_12 — — CNA_8_5 — — LOH_1_2 — — CNA_3_17 — — RPL7P9 653702 NC_000001; NG_007184 LOH_5_16 — — CNA_q_16 — — LOH_1_5 — — LOH_14_3 — — LOH_9_2 — — LOH_19_3 — — CNA_2_8 — — LOH_1_10 — — LOH_22_3 — — GPSM2 29899 NC_000001; NG_028108 LOH_3_0 — — CNA_10_4 — — LOH_1_6 — — CNA_6_8 — — CNA_3_2 — — CNA_3_8 — — CNA_p_17 — — CNA_7_13 — — LOH_q_9 — — CNA_7_0 — — CNA_1_2 — — CNA_3_0 — — CNA_18_3 — — NBPF8 728841 NC_000001 CNA_q_8 — — CNA_13_6 — — CNA_1_4 — — tmb — — CNA_7_3 — — CNA_3_11 — — LOH_1_3 — — LOH_1_8 — — LOH_18_7 — — AKNAD1 254268 NC_000001; NG_032762 CNA_1_23 — — LOH_q_16 — — CNA_17_1 — — CNA_17_5 — — LOH_10_11 — — LOH_11_8 — — CNA_5_12 — — CNA_2_6 — — CNA_13_4 — — CNA_3_12 — — CNA_q_20 — — CNA_18_7 — — CNA_19_4 — — CNA_1_15 — — CNA_11_6 — — LOH_10_2 — — CNA_6_0 — — CNA_17_7 — — CNA_1_3 — — NBPF5P 100507044 NG_028895; NW_017852928; NC_000001 CNA_3_15 — — CNA_11_10 — — CNA_22_3 — — LOH_3_15 — — CNA_1_8 — — CNA_11_3 — — LOH_8_0 — — LOH_1_1 — — CNA_1_11 — — CNA_19_5 — — CNA_3_14 — — TRGV8 6982 NC_000007; NG_001336 LOH_11_1 — — CNA_6_2 — — LOH_16_6 — — CNA_17_0 — — CNA_11_2 — — BRCA_tumor_non_basal CDH1_mut 999 NC_000016; NG_008021 TP53_mut 7157 NG_017013; NC_000017 PIK3CA_mut 5290 NC_000003; NG_012113 PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_16_8 — — CNA_3_14 — — CNA_19_5 — — CNA_3_17 — — CNA_p_7 — — CNA_17_5 — — CNA_14_3 — — CNA_q_16 — — CNA_16_0 — — CNA_5_7 — — CNA_5_8 — — CNA_11_11 — — MLST8 64223 NC_000016 SLC22A31 146429 NC_000016 RPA3 6119 NC_000007 MPDZ 8777 NC_000009; NG_042810 MIR29C 407026 NC_000001 NFIB 4781 NC_000009 LASP1 3927 NC_000017 SLC46A1 113235 NG_013306; NC_000017 CORO6 84940 NG_054920; NC_000017 RIMS3 9783 NC_000001 EYS 346007 NC_000006; NG_023443 OR6C76 390326 NC_000012 SPAG5-AS1 100506436 NC_000017 ZFP69 339559 NC_000001 SLBP 7884 NC_000004 TRAP1 10131 NC_000016; NG_033088 RIMS1 22999 NG_016209; NC_000006 CREBBP 1387 NG_009873; NC_000016 MTMR2 8898 NC_000011; NG_008333 RBFOX1 54715 NC_000016; NG_011881 KCNMB2-AS1 104797538 NC_000003 tmb — — BRCA_tumor_basal TP53_mut 7157 NG_017013; NC_000017 PIK3CA_mut 5290 NC_000003; NG_012113 BRCA1_mut 672 NG_005905; NC_000017 CNA_16_8 — — CNA_16_7 — — CNA_20_5 — — CNA_q_22 — — CNA_22_4 — — CNA_14_3 — — CNA_8_0 — — CNA_5_16 — — CNA_1_14 — — CNA_1_20 — — CNA_22_3 — — CNA_3_4 — — CNA_15_3 — — TTC30A 92104 NC_000002 SLC22A31 146429 NC_000016 RCL1 10171 NC_000009 SLC25A51P1 442229 NG_025947; NT_187555; NC_000006 LINC00304 283860 NC_000016 NOP14-AS1 317648 NC_000004 PKN2-AS1 101927891 NC_000001 MPDZ 8777 NC_000009; NG_042810 PKIB 5570 NC_000006 MMP25-AS1 100507419 NC_000016 NFIB 4781 NC_000009 TBC1D30 23329 NC_000012 NELL2 4753 NC_000012 ITGA7 3679 NC_000012; NG_012343 RUNX2 860 NG_008020; NC_000006 SH3GLB1 51100 NG_030018; NC_000001 NCOA7 135112 NC_000006 HIVEP3 59269 NC_000001; NG_030026 CSAD 51380 NC_000012; NG_030036 COL2A1 1280 NG_008072; NC_000012 ARL6IP1 23204 NG_042860; NC_000016 KRT76 51350 NC_000012; NG_012420 CREBBP 1387 NG_009873; NC_000016 CDCP2 200008 NC_000001 tmb — — OV_tumor — — TP53_mut 7157 NG_017013; NC_000017 PIK3CA_mut 5290 NC_000003; NG_012113 MST1R_mut 4486 NC_000003; NG_030322 MGA_mut 23269 NC_000015 PIK3CA_hotspots 5290 NC_000003; NG_012113 CNA_3_14 — — CNA_19_5 — — CNA_3_17 — — CNA_22_4 — — CNA_17_7 — — CNA_6_15 — — CNA_11_12 — — CNA_11_10 — — CNA_16_0 — — CNA_5_7 — — CNA_7_0 — — CNA_3_4 — — CNA_12_5 — — CNA_3_19 — — TTC30A 92104 NC_000002 TTC14 151613 NC_000003 MYO1D 4642 NC_000017 SLC22A31 146429 NC_000016 ZFP69B 65243 NC_000001 EMP2 2013 NG_042058; NC_000016 SUPT6H 6830 NC_000017 NELL2 4753 NC_000012 MIR548AJ1 100616191 NC_000006 LYRM1 57149 NC_000016 PKD1 5310 NC_000016; NG_008617; NT_187607 ESPL1 9700 NC_000012 INSL6 11172 NG_046969; NC_000009 HHAT 55733 NW_011332687; NC_000001 CD46 4179 NC_000001; NG_009296 ERBB3 2065 NC_000012; NG_011529 SMARCE1 6605 NG_032163; NC_000017 KCNMB2-AS1 104797538 NC_000003 SSH2 85464 NC_000017 FAM53A 152877 NC_000004; NW_021159990 tmb — — Squamous_Cell_Lung_Carcinoma CNTNAP5_mut 129684 NC_000002 CPS1_mut 1373 NC_000002; NG_008285 LRRC7_mut 57554 NC_000001 PIK3CA_mut 5290 NC_000003; NG_012113 RYR2_mut 6262 NG_008799; NC_000001 TP53_mut 7157 NG_017013; NC_000017 USH2A_mut 7399 NC_000001; NG_009497 CNA_1_15 — — CNA_1_17 — — CNA_2_6 — — LOH_2_21 — — LOH_3_3 — — LOH_3_5 — — LOH_3_6 — — CNA_3_8 — — CNA_3_18 — — CNA_3_19 — — CNA_4_3 — — CNA_4_8 — — CNA_4_11 — — CNA_4_13 — — CNA_4_18 — — CNA_5_0 — — CNA_5_10 — — LOH_5_10 — — CNA_5_11 — — CNA_5_12 — — CNA_5_13 — — CNA_5_17 — — CNA_7_0 — — CNA_7_4 — — CNA_8_1 — — CNA_8_2 — — CNA_8_6 — — CNA_8_7 — — CNA_8_13 — — CNA_9_1 — — LOH_9_1 — — LOH_9_11 — — LOH_9_12 — — LOH_9_13 — — CNA_10_10 — — CNA_11_11 — — LOH_11_11 — — CNA_11_12 — — CNA_12_2 — — LOH_13_3 — — CNA_13_5 — — CNA_13_7 — — LOH_13_7 — — CNA_13_10 — — CNA_14_5 — — CNA_14_9 — — LOH_17_0 — — CNA_20_5 — — CNA_p_5 — — CNA_q_8 — — PPIAP78 202227 NC_000005; NG_030081 tmb — — Squamous_Cell_Carcinoma_of_the_Head_and_Neck CDKN2A_mut 1029 NC_000009; NG_007485 PTEN_mut 5728 NC_000010; NW_013171807; NG_007466 RYR2_mut 6262 NG_008799; NC_000001 TP53_mut 7157 NG_017013; NC_000017 USH2A_mut 7399 NC_000001; NG_009497 TP53_hotspots 7157 NG_017013; NC_000017 CNA_1_16 — — CNA_1_18 — — CNA_1_20 — — CNA_2_6 — — CNA_2_21 — — CNA_2_22 — — CNA_3_0 — — CNA_3_1 — — LOH_3_1 — — CNA_3_3 — — LOH_3_3 — — CNA_3_4 — — LOH_3_6 — — LOH_3_7 — — CNA_3_8 — — LOH_3_8 — — CNA_3_13 — — CNA_3_19 — — CNA_4_2 — — CNA_4_8 — — CNA_4_13 — — CNA_4_17 — — CNA_4_18 — — CNA_5_0 — — CNA_5_1 — — CNA_5_3 — — CNA_5_6 — — LOH_5_6 — — LOH_5_10 — — CNA_5_11 — — CNA_5_12 — — CNA_5_13 — — CNA_5_17 — — CNA_6_14 — — CNA_7_0 — — CNA_7_3 — — CNA_7_4 — — CNA_8_2 — — CNA_8_5 — — CNA_8_6 — — CNA_8_7 — — CNA_8_9 — — CNA_8_11 — — CNA_8_12 — — CNA_8_13 — — CNA_9_1 — — LOH_9_1 — — LOH_9_11 — — LOH_9_12 — — CNA_10_10 — — CNA_10_11 — — CNA_11_12 — — CNA_12_2 — — LOH_13_2 — — CNA_13_3 — — LOH_13_3 — — CNA_13_5 — — LOH_13_5 — — LOH_13_6 — — CNA_13_7 — — LOH_13_7 — — CNA_13_9 — — CNA_14_5 — — LOH_17_0 — — CNA_18_4 — — CNA_18_5 — — CNA_18_6 — — CNA_p_5 — — CNA_q_20 — — CNA_q_8 — — GAL 51083 NC_000011; NG_052785 CPT1A 1374 NC_000011; NG_011801 SHANK2 22941 NG_042866; NC_000011 NADSYN1 55191 NC_000011 TRPS1 7227 NC_000008; NG_012383 PHF20L1 51105 NC_000008 tmb — — Cervical_Squamous_Cell_Carcinoma CSMD3_mut 114788 NC_000008 TP53_mut 7157 NG_017013; NC_000017 TP53_hotspots 7157 NG_017013; NC_000017 CNA_1_15 — — CNA_1_20 — — CNA_2_21 — — CNA_3_4 — — CNA_3_5 — — CNA_5_3 — — CNA_6_14 — — CNA_7_0 — — CNA_8_11 — — LOH_9_1 — — LOH_9_13 — — CNA_11_12 — — CNA_12_1 — — LOH_13_5 — — CNA_13_9 — — CNA_18_5 — — CDKN2A 1029 NC_000009; NG_007485 CDKN2B 1030 NC_000009; NG_023297 tmb — — Glioblastoma CNA_p_10 — — AC073324.1 102725541 NC_000007 tmb — — CNA_10_0 — — LOH_19_5 — — LOH_10_5 — — IDH1_mut 3417 NG_023319; NC_000002 LOH_q_10 — — Astrocytoma CNA_1_1 — — LOH_p_10 — — TP53_mut 7157 NG_017013; NC_000017 CNA_1_5 — — IDH1_mut 3417 NG_023319; NC_000002 LOH_1_7 — — Oligodendroglioma LOH_q_10 — — ATG4C 84938 NC_000001 LOH_1_0 — — LOH_10_4 — — TP53_mut 7157 NG_017013; NC_000017 CNA_1_5 — — LOH_1_6 — — LOH_1_7 — — Stomach_Adenocarcinoma CNA_13_9 — — AL627224.2 — — KRAS_p.G12 3845 NC_000012; NG_007524 LOH_9_1 — — CNA_8_1 — — KRAS_mut 3845 NC_000012; NG_007524 AL807743.1 — — tmb — — APC_mut 324 NG_008481; NC_000005 BRAF_hotspots 673 NC_000007; NG_007873 CNA_p_18 — — Colorectal_Adenocarcinoma CNA_13_9 — — AL627224.2 — — KRAS_p.G12 3845 NC_000012; NG_007524 LOH_9_1 — — CNA_8_1 — — KRAS_mut 3845 NC_000012; NG_007524 AL807743.1 — — tmb — — APC_mut 324 NG_008481; NC_000005 BRAF_hotspots 673 NC_000007; NG_007873 CNA_p_18 — — Clear_Cell_Renal_Cell_Carcinoma VHL_mut 7428 NC_000003; NG_008212 CNA_1_3 — — CNA_3_0 — — LOH_3_0 — — LOH_3_2 — — CNA_3_4 — — CNA_5_14 — — CNA_5_17 — — CNA_8_2 — — CNA_16_4 — — CNA_17_4 — — CNA_q_14 — — CNA_q_17 — — LOH_p_1 — — PRSS50 29122 NC_000003 tmb — — Papillary_Renal_Cell_Carcinoma VHL_mut 7428 NC_000003; NG_008212 CNA_1_3 — — CNA_1_10 — — LOH_2_3 — — CNA_3_0 — — LOH_3_0 — — CNA_3_2 — — LOH_3_2 — — CNA_3_3 — — CNA_3_4 — — LOH_3_4 — — CNA_3_5 — — LOH_3_5 — — CNA_3_6 — — LOH_3_6 — — CNA_3_8 — — CNA_5_14 — — CNA_5_15 — — CNA_5_17 — — LOH_6_3 — — CNA_7_3 — — CNA_7_14 — — CNA_7_15 — — CNA_8_2 — — LOH_10_4 — — CNA_13_2 — — LOH_13_3 — — LOH_14_6 — — CNA_16_4 — — LOH_17_1 — — CNA_17_3 — — CNA_17_4 — — CNA_17_5 — — CNA_17_7 — — LOH_17_7 — — LOH_18_4 — — CNA_p_1 — — CNA_p_2 — — CNA_p_3 — — CNA_q_10 — — CNA_q_14 — — CNA_q_17 — — LOH_p_1 — — LOH_p_2 — — LOH_p_3 — — LOH_p_6 — — LOH_q_1 — — PRSS50 29122 NC_000003 TRGV9 6983 NG_001336; NC_000007 tmb — — Chromophobe_Renal_Cell_Carcinoma LOH_1_5 — — LOH_1_9 — — CNA_1_17 — — LOH_1_19 — — CNA_5_16 — — CNA_6_3 — — CNA_10_1 — — LOH_10_2 — — LOH_13_4 — — LOH_14_5 — — CNA_17_3 — — LOH_17_7 — — CNA_p_1 — — CNA_p_3 — — CNA_q_1 — — CNA_q_17 — — CNA_q_6 — — LOH_p_1 — — LOH_p_3 — — LOH_q_1 — — LOH_q_6 — — tmb — —

Biological Samples

Any of the methods, systems, or other claimed elements may use or be used to analyze a biological sample from a subject. In some embodiments, a biological sample is obtained from a subject having, suspected of having cancer, or at risk of having cancer. The biological sample may be any type of biological sample including, for example, a biological sample of a bodily fluid (e.g., blood, urine or cerebrospinal fluid), one or more cells (e.g., from a scraping or brushing such as a cheek swab or tracheal brushing), a piece of tissue (cheek tissue, muscle tissue, lung tissue, heart tissue, brain tissue, or skin tissue), or some or all of an organ (e.g., brain, lung, liver, bladder, kidney, pancreas, intestines, or muscle), or other types of biological samples (e.g., feces or hair).

In some embodiments, the biological sample is a sample of a tumor from a subject. In some embodiments, the biological sample is a sample of blood from a subject. In some embodiments, the biological sample is a sample of tissue from a subject.

A sample of a tumor, in some embodiments, refers to a sample comprising cells from a tumor. In some embodiments, the sample of the tumor comprises cells from a benign tumor, e.g., non-cancerous cells. In some embodiments, the sample of the tumor comprises cells from a premalignant tumor, e.g., precancerous cells. In some embodiments, the sample of the tumor comprises cells from a malignant tumor, e.g., cancerous cells. In some embodiments, the sample of tumor can include a mixture of cancerous, non-cancerous, and/or precancerous cells.

Examples of tumors include, but are not limited to, adenomas, fibromas, hemangiomas, lipomas, cervical dysplasia, metaplasia of the lung, leukoplakia, carcinoma, sarcoma, germ cell tumors, melanomas, mesotheliomas, gliomas, and blastoma.

A sample of blood, in some embodiments, refers to a sample comprising cells, e.g., cells from a blood sample. In some embodiments, the sample of blood comprises non-cancerous cells. In some embodiments, the sample of blood comprises precancerous cells. In some embodiments, the sample of blood comprises cancerous cells. In some embodiments, the sample of blood comprises blood cells. In some embodiments, the sample of blood comprises red blood cells. In some embodiments, the sample of blood comprises white blood cells. In some embodiments, the sample of blood comprises platelets. Examples of cancerous blood cells include, but are not limited to, leukemia, lymphoma, and myeloma. In some embodiments, a sample of blood is collected to obtain the cell-free nucleic acid (e.g., cell-free DNA) in the blood.

A sample of blood may be a sample of whole blood or a sample of fractionated blood. In some embodiments, the sample of blood comprises whole blood. In some embodiments, the sample of blood comprises fractionated blood. In some embodiments, the sample of blood comprises buffy coat. In some embodiments, the sample of blood comprises serum. In some embodiments, the sample of blood comprises plasma. In some embodiments, the sample of blood comprises a blood clot.

A sample of a tissue, in some embodiments, refers to a sample comprising cells from a tissue. In some embodiments, the sample of the tumor comprises non-cancerous cells from a tissue. In some embodiments, the sample of the tumor comprises precancerous cells from a tissue. In some embodiments, the sample of the tumor comprises cancerous tissue. In some embodiments, the sample can comprise cancerous, precancerous, or non-cancerous cells.

Methods of the present disclosure encompass a variety of tissue including organ tissue or non-organ tissue, including but not limited to, muscle tissue, brain tissue, lung tissue, liver tissue, epithelial tissue, connective tissue, and nervous tissue. In some embodiments, the tissue may be normal tissue or it may be diseased tissue or it may be tissue suspected of being diseased. In some embodiments, the tissue may be sectioned tissue or whole intact tissue. In some embodiments, the tissue may be animal tissue or human tissue. Animal tissue includes, but is not limited to, tissues obtained from rodents (e.g., rats or mice), primates (e.g., monkeys), dogs, cats, and farm animals.

The biological sample may be from any source in the subject's body including, but not limited to, any fluid [such as blood (e.g., whole blood, blood serum, or blood plasma), saliva, tears, synovial fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, ascitic fluid, and/or urine], hair, skin (including portions of the epidermis, dermis, and/or hypodermis), oropharynx, laryngopharynx, esophagus, stomach, bronchus, salivary gland, tongue, oral cavity, nasal cavity, vaginal cavity, anal cavity, bone, bone marrow, brain, thymus, spleen, small intestine, appendix, colon, rectum, anus, liver, biliary tract, pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva, ovary, cervix, scrotum, penis, prostate, testicle, seminal vesicles, and/or any type of tissue (e.g., muscle tissue, epithelial tissue, connective tissue, or nervous tissue).

Any of the biological samples described herein may be obtained from the subject using any known technique. See, for example, the following publications on collecting, processing, and storing biological samples, each of which are incorporated herein in its entirety: Biospecimens and biorepositories: from afterthought to science by Vaught et al. (Cancer Epidemiol Biomarkers Prev. 2012 February; 21(2):253-5), and Biological sample collection, processing, storage and information management by Vaught and Henderson (IARC Sci Publ. 2011; (163):23-42).

In some embodiments, the biological sample may be obtained from a surgical procedure (e.g., laparoscopic surgery, microscopically controlled surgery, or endoscopy), bone marrow biopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g., a fine-needle aspiration, core needle biopsy, vacuum-assisted biopsy, or image-guided biopsy).

In some embodiments, one or more than one cell (a cell biological sample) may be obtained from a subject using a scrape or brush method. The cell biological sample may be obtained from any area in or from the body of a subject including, for example, from one or more of the following areas: the cervix, esophagus, stomach, bronchus, or oral cavity. In some embodiments, one or more than one piece of tissue (e.g., a tissue biopsy) from a subject may be used. In certain embodiments, the tissue biopsy may comprise one or more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) biological samples from one or more tumors or tissues known or suspected of having cancerous cells.

Any of the biological samples from a subject described herein may be stored using any method that preserves stability of the biological sample. In some embodiments, preserving the stability of the biological sample means inhibiting components (e.g., DNA, RNA, protein, or tissue structure or morphology) of the biological sample from degrading until they are measured so that when measured, the measurements represent the state of the sample at the time of obtaining it from the subject. In some embodiments, a biological sample is stored in a composition that is able to penetrate the same and protect components (e.g., DNA, RNA, protein, or tissue structure or morphology) of the biological sample from degrading. As used herein, degradation is the transformation of a component from one from to another such that the first form is no longer detected at the same level as before degradation.

In some embodiments, a biological sample (e.g., tissue sample) is fixed. As used herein, a “fixed” sample relates to a sample that has been treated with one or more agents or processes in order to prevent or reduce decay or degradation, such as autolysis or putrefaction, of the sample. Examples of fixative processes include but are not limited to heat fixation, immersion fixation, and perfusion. In some embodiments a fixed sample is treated with one or more fixative agents. Examples of fixative agents include but are not limited to cross-linking agents (e.g., aldehydes, such as formaldehyde, formalin, glutaraldehyde, etc.), precipitating agents (e.g., alcohols, such as ethanol, methanol, acetone, xylene, etc.), mercurials (e.g., B-5, Zenker's fixative, etc.), picrates, and Hepes-glutamic acid buffer-mediated organic solvent protection effect (HOPE) fixatuve. In some embodiments, a biological sample (e.g., tissue sample) is treated with a cross-linking agent. In some embodiments, the cross-linking agent comprises formalin. In some embodiments, a formalin-fixed biological sample is embedded in a solid substrate, for example paraffin wax. In some embodiments, the biological sample is a formalin-fixed paraffin-embedded (FFPE) sample. Methods of preparing FFPE samples are known, for example as described by Li et al. JCO Precis Oncol. 2018; 2: PO.17.00091.

In some embodiments, the biological sample is stored using cryopreservation. Non-limiting examples of cryopreservation include, but are not limited to, step-down freezing, blast freezing, direct plunge freezing, snap freezing, slow freezing using a programmable freezer, and vitrification. In some embodiments, the biological sample is stored using lyophilization. In some embodiments, a biological sample is placed into a container that already contains a preservant (e.g., RNALater to preserve RNA) and then frozen (e.g., by snap-freezing), after the collection of the biological sample from the subject. In some embodiments, such storage in frozen state is done immediately after collection of the biological sample. In some embodiments, a biological sample may be kept at either room temperature or 4° C. for some time (e.g., up to an hour, up to 8 h, or up to 1 day, or a few days) in a preservant or in a buffer without a preservant, before being frozen.

Non-limiting examples of preservants include formalin solutions, formaldehyde solutions, RNALater or other equivalent solutions, TriZol or other equivalent solutions, DNA/RNA Shield or equivalent solutions, EDTA (e.g., Buffer AE (10 mM Tris·Cl; 0.5 mM EDTA, pH 9.0)) and other coagulants, and Acids Citrate Dextronse (e.g., for blood specimens).

In some embodiments, special containers may be used for collecting and/or storing a biological sample. For example, a vacutainer may be used to store blood. In some embodiments, a vacutainer may comprise a preservant (e.g., a coagulant, or an anticoagulant). In some embodiments, a container in which a biological sample is preserved may be contained in a secondary container, for the purpose of better preservation, or for the purpose of avoid contamination.

Any of the biological samples from a subject described herein may be stored under any condition that preserves stability of the biological sample. In some embodiments, the biological sample is stored at a temperature that preserves stability of the biological sample. In some embodiments, the sample is stored at room temperature (e.g., 25° C.). In some embodiments, the sample is stored under refrigeration (e.g., 4° C.). In some embodiments, the sample is stored under freezing conditions (e.g., −20° C.). In some embodiments, the sample is stored under ultralow temperature conditions (e.g., −50° C. to −800° C.). In some embodiments, the sample is stored under liquid nitrogen (e.g., −1700° C.). In some embodiments, a biological sample is stored at −60° C. to −80° C. (e.g., −70° C.) for up to 5 years (e.g., up to 1 month, up to 2 months, up to 3 months, up to 4 months, up to 5 months, up to 6 months, up to 7 months, up to 8 months, up to 9 months, up to 10 months, up to 11 months, up to 1 year, up to 2 years, up to 3 years, up to 4 years, or up to 5 years). In some embodiments, a biological sample is stored as described by any of the methods described herein for up to 20 years (e.g., up to 5 years, up to 10 years, up to 15 years, or up to 20 years).

Methods of the present disclosure encompass obtaining one or more biological samples from a subject for analysis. In some embodiments, one biological sample is collected from a subject for analysis. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) biological samples are collected from a subject for analysis. In some embodiments, one biological sample from a subject will be analyzed. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) biological samples may be analyzed. If more than one biological sample from a subject is analyzed, the biological samples may be procured at the same time (e.g., more than one biological sample may be taken in the same procedure), or the biological samples may be taken at different times (e.g., during a different procedure including a procedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 decades after a first procedure).

A second or subsequent biological sample may be taken or obtained from the same region (e.g., from the same tumor or area of tissue) or a different region (including, e.g., a different tumor). A second or subsequent biological sample may be taken or obtained from the subject after one or more treatments and may be taken from the same region or a different region. As a non-limiting example, the second or subsequent biological sample may be useful in determining whether the cancer in each biological sample has different characteristics (e.g., in the case of biological samples taken from two physically separate tumors in a patient) or whether the cancer has responded to one or more treatments (e.g., in the case of two or more biological samples from the same tumor or different tumors prior to and subsequent to a treatment). In some embodiments, each of the at least one biological sample is a bodily fluid sample, a cell sample, or a tissue biopsy sample.

In some embodiments, one or more biological specimens are combined (e.g., placed in the same container for preservation) before further processing. For example, a first sample of a first tumor obtained from a subject may be combined with a second sample of a second tumor from the subject, wherein the first and second tumors may or may not be the same tumor. In some embodiments, a first tumor and a second tumor are similar but not the same (e.g., two tumors in the brain of a subject). In some embodiments, a first biological sample and a second biological sample from a subject are sample of different types of tumors (e.g., a tumor in muscle tissue and brain tissue).

In some embodiments, a sample from which RNA and/or DNA is extracted (e.g., a sample of tumor, or a blood sample) is sufficiently large such that at least 2 μg (e.g., at least 2 μg, at least 2.5 μg, at least 3 μg, at least 3.5 μg or more) of RNA can be extracted from it. In some embodiments, the sample from which RNA and/or DNA is extracted can be peripheral blood mononuclear cells (PBMCs). In some embodiments, the sample from which RNA and/or DNA is extracted can be any type of cell suspension. In some embodiments, a sample from which RNA and/or DNA is extracted (e.g., a sample of tumor, or a blood sample) is sufficiently large such that at least 1.8 μg RNA can be extracted from it. In some embodiments, at least 50 mg (e.g., at least 1 mg, at least 2 mg, at least 3 mg, at least 4 mg, at least 5 mg, at least 10 mg, at least 12 mg, at least 15 mg, at least 18 mg, at least 20 mg, at least 22 mg, at least 25 mg, at least 30 mg, at least 35 mg, at least 40 mg, at least 45 mg, or at least 50 mg) of tissue sample is collected from which RNA and/or DNA is extracted. In some embodiments, at least 20 mg of tissue sample is collected from which RNA and/or DNA is extracted. In some embodiments, at least 30 mg of tissue sample is collected. In some embodiments, at least 10-50 mg (e.g., 10-50 mg, 10-15 mg, 10-30 mg, 10-40 mg, 20-30 mg, 20-40 mg, 20-50 mg, or 30-50 mg) of tissue sample is collected from which RNA and/or DNA is extracted. In some embodiments, at least 30 mg of tissue sample is collected. In some embodiments, at least 20-30 mg of tissue sample is collected from which RNA and/or DNA is extracted. In some embodiments, a sample from which RNA and/or DNA is extracted (e.g., a sample of tumor, or a blood sample) is sufficiently large such that at least 0.2 μg (e.g., at least 200 ng, at least 300 ng, at least 400 ng, at least 500 ng, at least 600 ng, at least 700 ng, at least 800 ng, at least 900 ng, at least 1 μg, at least 1.1 μg, at least 1.2 μg, at least 1.3 μg, at least 1.4 μg, at least 1.5 μg, at least 1.6 μg, at least 1.7 μg, at least 1.8 μg, at least 1.9 μg, or at least 2 μg) of RNA can be extracted from it. In some embodiments, a sample from which RNA and/or DNA is extracted (e.g., a sample of tumor, or a blood sample) is sufficiently large such that at least 0.1 μg (e.g., at least 100 ng, at least 200 ng, at least 300 ng, at least 400 ng, at least 500 ng, at least 600 ng, at least 700 ng, at least 800 ng, at least 900 ng, at least 1 μg, at least 1.1 μg, at least 1.2 μg, at least 1.3 μg, at least 1.4 μg, at least 1.5 μg, at least 1.6 μg, at least 1.7 μg, at least 1.8 μg, at least 1.9 μg, or at least 2 μg) of RNA can be extracted from it.

Subjects

Aspects of this disclosure relate to a biological sample that has been obtained from a subject. In some embodiments, a subject is a mammal (e.g., a human, a mouse, a cat, a dog, a horse, a hamster, a cow, a pig, or other domesticated animal). In some embodiments, a subject is a human. In some embodiments, a subject is an adult human (e.g., of 18 years of age or older). In some embodiments, a subject is a child (e.g., less than 18 years of age). In some embodiments, a human subject is one who has or has been diagnosed with at least one form of cancer.

In some embodiments, a cancer from which a subject suffers is a carcinoma, a sarcoma, a myeloma, a leukemia, a lymphoma, a melanoma, a mesothelioma, a glioma, or a mixed type of cancer that comprises more than one of a carcinoma, a sarcoma, a myeloma, a leukemia, and a lymphoma. Carcinoma refers to a malignant neoplasm of epithelial origin or cancer of the internal or external lining of the body. Sarcoma refers to cancer that originates in supportive and connective tissues such as bones, tendons, cartilage, muscle, and fat. Myeloma is cancer that originates in the plasma cells of bone marrow. Leukemias (“liquid cancers” or “blood cancers”) are cancers of the bone marrow (the site of blood cell production). Lymphomas develop in the glands or nodes of the lymphatic system, a network of vessels, nodes, and organs (specifically the spleen, tonsils, and thymus) that purify bodily fluids and produce infection-fighting white blood cells, or lymphocytes. Melanoma is a type of skin cancer that originates in the melanocytes of the skin. Mesothelioma's cancers arise from the mesothelium, which forms the lining of organs and cavities, such as, for example, the lungs and the abdomen. Glioma develops in the brain, and specifically in the glial cells, which provide physical and metabolic support to neurons. Non-limiting examples of a mixed type of cancer include adenosquamous carcinoma, mixed mesodermal tumor, carcinosarcoma, and teratocarcinoma. In some embodiments, a subject has a tumor. A tumor may be benign or malignant.

In some embodiments, a cancer is any one of the following: skin cancer, lung cancer, breast cancer, prostate cancer, colon cancer, pancreatic cancer, rectal cancer, cervical cancer, and cancer of the uterus. In some embodiments, a subject is at risk for developing cancer, e.g., because the subject has one or more genetic risk factors, or has been exposed to or is being exposed to one or more carcinogens (e.g., cigarette smoke, or chewing tobacco).

Expression Data

Expression data (e.g., indicating expression levels) for a plurality of genes may be used for any of the methods or compositions described herein. The number of genes which may be examined may be up to and inclusive of all the genes of the subject. In some embodiments, expression levels may be examined for all of the genes of a subject. As a non-limiting example, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 35 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more, 200 or more, 225 or more, 250 or more, 275 or more, or 300 or more genes may be used for any evaluation described herein. As another set of non-limiting examples, the expression data may include, for each molecular category listed in Table 2, expression data for at least 5, at least 10, at least 15, at least 20, at least 25, at least 35, at least 50, at least 75, at least 100 genes selected from the group of genes for that molecular category in Table 2.

Any method may be used on a sample from a subject in order to acquire expression data (e.g., indicating expression levels) for the plurality of genes. As a set of non-limiting examples, the expression data may be RNA expression data, DNA expression data, or protein expression data.

DNA expression data, in some embodiments, refers to a level of DNA (e.g., copy number of a chromosome, gene, or other genomic region) in a sample from a subject. The level of DNA in a sample from a subject having cancer may be elevated compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene duplication in a cancer patient's sample. The level of DNA in a sample from a subject having cancer may be reduced compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene deletion in a cancer patient's sample.

DNA expression data, in some embodiments, refers to data (e.g., sequencing data) for DNA (e.g., coding or non-coding genomic DNA) present in a sample, for example, sequencing data for a gene that is present in a patient's sample. DNA that is present in a sample may or may not be transcribed, but it may be sequenced using DNA sequencing platforms. Such data may be useful, in some embodiments, to determine whether the patient has one or more mutations associated with a particular cancer.

RNA expression data may be acquired using any method known in the art including, but not limited to: whole transcriptome sequencing, total RNA sequencing, mRNA sequencing, targeted RNA sequencing, small RNA sequencing, ribosome profiling, RNA exome capture sequencing, and/or deep RNA sequencing. DNA expression data may be acquired using any method known in the art including any known method of DNA sequencing. For example, DNA sequencing may be used to identify one or more mutations in the DNA of a subject. Any technique used in the art to sequence DNA may be used with the methods and compositions described herein. As a set of non-limiting examples, the DNA may be sequenced through single-molecule real-time sequencing, ion torrent sequencing, pyrosequencing, sequencing by synthesis, sequencing by ligation (SOLiD sequencing), nanopore sequencing, or Sanger sequencing (chain termination sequencing). Protein expression data may be acquired using any method known in the art including, but not limited to: N-terminal amino acid analysis, C-terminal amino acid analysis, Edman degradation (including though use of a machine such as a protein sequenator), or mass spectrometry.

In some embodiments, the expression data is acquired through bulk RNA sequencing. Bulk RNA sequencing may include obtaining expression levels for each gene across RNA extracted from a large population of input cells (e.g., a mixture of different cell types.) In some embodiments, the expression data is acquired through single cell sequencing (e.g., scRNA-seq). Single cell sequencing may include sequencing individual cells

In some embodiments, the expression data comprises whole exome sequencing (WES) data. In some embodiments, the expression data comprises whole genome sequencing (WGS) data. In some embodiments, the expression data comprises next-generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data.

Obtaining RNA Expression Data

In some embodiments, a method to process RNA expression data (e.g., data obtained from RNA sequencing (also referred to herein as RNA-seq data)) comprises obtaining RNA expression data for a subject (e.g., a subject who has or has been diagnosed with a cancer). In some embodiments, obtaining RNA expression data comprises obtaining a biological sample and processing it to perform RNA sequencing using any one of the RNA sequencing methods described herein. In some embodiments, RNA expression data is obtained from a lab or center that has performed experiments to obtain RNA expression data (e.g., a lab or center that has performed RNA-seq). In some embodiments, a lab or center is a medical lab or center.

In some embodiments, RNA expression data is obtained by obtaining a computer storage medium (e.g., a data storage drive) on which the data exists. In some embodiments, RNA expression data is obtained via a secured server (e.g., a SFTP server, or Illumina BaseSpace). In some embodiments, data is obtained in the form of a text-based filed (e.g., a FASTQ file). In some embodiments, a file in which sequencing data is stored also contains quality scores of the sequencing data). In some embodiments, a file in which sequencing data is stored also contains sequence identifier information.

Methods of Treatment

In certain methods described herein, an effective amount of anti-cancer therapy described herein may be administered or recommended for administration to a subject (e.g., a human) in need of the treatment via a suitable route (e.g., intravenous administration).

The subject to be treated by the methods described herein may be a human patient having, suspected of having, or at risk for a cancer. Examples of a cancer include, but are not limited to, melanoma, lung cancer, brain cancer, breast cancer, colorectal cancer, pancreatic cancer, liver cancer, prostate cancer, skin cancer, kidney cancer, bladder cancer, or prostate cancer. At the time of diagnosis the cancer may be cancer of unknown primary. The subject to be treated by the methods described herein may be a mammal (e.g., may be a human). Mammals include, but are not limited to: farm animals (e.g., livestock), sport animals, laboratory animals, pets, primates, horses, dogs, cats, mice, and rats.

A subject having a cancer may be identified by routine medical examination, e.g., laboratory tests, biopsy, PET scans, CT scans, or ultrasounds. A subject suspected of having a cancer might show one or more symptoms of the disorder, e.g., unexplained weight loss, fever, fatigue, cough, pain, skin changes, unusual bleeding or discharge, and/or thickening or lumps in parts of the body. A subject at risk for a cancer may be a subject having one or more of the risk factors for that disorder. For example, risk factors associated with cancer include, but are not limited to, (a) viral infection (e.g., herpes virus infection), (b) age, (c) family history, (d) heavy alcohol consumption, (e) obesity, and (f) tobacco use.

“An effective amount” as used herein refers to the amount of each active agent required to confer therapeutic effect on the subject, either alone or in combination with one or more other active agents. Effective amounts vary, as recognized by those skilled in the art, depending on the particular condition being treated, the severity of the condition, the individual patient parameters including age, physical condition, size, gender and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment. It will be understood by those of ordinary skill in the art, however, that a patient may insist upon a lower dose or tolerable dose for medical reasons, psychological reasons, or for virtually any other reasons.

Empirical considerations, such as the half-life of a therapeutic compound, generally contribute to the determination of the dosage. For example, antibodies that are compatible with the human immune system, such as humanized antibodies or fully human antibodies, may be used to prolong half-life of the antibody and to prevent the antibody being attacked by the host's immune system. Frequency of administration may be determined and adjusted over the course of therapy, and is generally (but not necessarily) based on treatment, and/or suppression, and/or amelioration, and/or delay of a cancer. Alternatively, sustained continuous release formulations of an anti-cancer therapeutic agent may be appropriate. Various formulations and devices for achieving sustained release are known in the art.

In some embodiments, dosages for an anti-cancer therapeutic agent as described herein may be determined empirically in individuals who have been administered one or more doses of the anti-cancer therapeutic agent. Individuals may be administered incremental dosages of the anti-cancer therapeutic agent. To assess efficacy of an administered anti-cancer therapeutic agent, one or more aspects of a cancer (e.g., tumor formation, tumor growth, molecular category identified for the cancer using the techniques described herein) may be analyzed.

Generally, for administration of any of the anti-cancer antibodies described herein, an initial candidate dosage may be about 2 mg/kg. For the purpose of the present disclosure, a typical daily dosage might range from about any of 0.1 μg/kg to 3 μg/kg to 30 μg/kg to 300 μg/kg to 3 mg/kg, to 30 mg/kg to 100 mg/kg or more, depending on the factors mentioned above. For repeated administrations over several days or longer, depending on the condition, the treatment is sustained until a desired suppression or amelioration of symptoms occurs or until sufficient therapeutic levels are achieved to alleviate a cancer, or one or more symptoms thereof. An exemplary dosing regimen comprises administering an initial dose of about 2 mg/kg, followed by a weekly maintenance dose of about 1 mg/kg of the antibody, or followed by a maintenance dose of about 1 mg/kg every other week. However, other dosage regimens may be useful, depending on the pattern of pharmacokinetic decay that the practitioner (e.g., a medical doctor) wishes to achieve. For example, dosing from one-four times a week is contemplated. In some embodiments, dosing ranging from about 3 μg/mg to about 2 mg/kg (such as about 3 μg/mg, about 10 μg/mg, about 30 μg/mg, about 100 μg/mg, about 300 μg/mg, about 1 mg/kg, and about 2 mg/kg) may be used. In some embodiments, dosing frequency is once every week, every 2 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks, every 8 weeks, every 9 weeks, or every 10 weeks; or once every month, every 2 months, or every 3 months, or longer. The progress of this therapy may be monitored by conventional techniques and assays. The dosing regimen (including the therapeutic used) may vary over time.

When the anti-cancer therapeutic agent is not an antibody, it may be administered at the rate of about 0.1 to 300 mg/kg of the weight of the patient divided into one to three doses, or as disclosed herein. In some embodiments, for an adult patient of normal weight, doses ranging from about 0.3 to 5.00 mg/kg may be administered. The particular dosage regimen, e.g., dose, timing, and/or repetition, will depend on the particular subject and that individual's medical history, as well as the properties of the individual agents (such as the half-life of the agent, and other considerations well known in the art).

For the purpose of the present disclosure, the appropriate dosage of an anti-cancer therapeutic agent will depend on the specific anti-cancer therapeutic agent(s) (or compositions thereof) employed, the type and severity of cancer, whether the anti-cancer therapeutic agent is administered for preventive or therapeutic purposes, previous therapy, the patient's clinical history and response to the anti-cancer therapeutic agent, and the discretion of the attending physician. Typically the clinician will administer an anti-cancer therapeutic agent, such as an antibody, until a dosage is reached that achieves the desired result.

Administration of an anti-cancer therapeutic agent can be continuous or intermittent, depending, for example, upon the recipient's physiological condition, whether the purpose of the administration is therapeutic or prophylactic, and other factors known to skilled practitioners. The administration of an anti-cancer therapeutic agent (e.g., an anti-cancer antibody) may be essentially continuous over a preselected period of time or may be in a series of spaced dose, e.g., either before, during, or after developing cancer.

As used herein, the term “treating” refers to the application or administration of a composition including one or more active agents to a subject, who has a cancer, a symptom of a cancer, or a predisposition toward a cancer, with the purpose to cure, heal, alleviate, relieve, alter, remedy, ameliorate, improve, or affect the cancer or one or more symptoms of the cancer, or the predisposition toward a cancer.

Alleviating a cancer includes delaying the development or progression of the disease, or reducing disease severity. Alleviating the disease does not necessarily require curative results. As used therein, “delaying” the development of a disease (e.g., a cancer) means to defer, hinder, slow, retard, stabilize, and/or postpone progression of the disease. This delay can be of varying lengths of time, depending on the history of the disease and/or individuals being treated. A method that “delays” or alleviates the development of a disease, or delays the onset of the disease, is a method that reduces probability of developing one or more symptoms of the disease in a given period and/or reduces extent of the symptoms in a given time frame, when compared to not using the method. Such comparisons are typically based on clinical studies, using a number of subjects sufficient to give a statistically significant result.

“Development” or “progression” of a disease means initial manifestations and/or ensuing progression of the disease. Development of the disease can be detected and assessed using clinical techniques known in the art. However, development also refers to progression that may be undetectable. For purpose of this disclosure, development or progression refers to the biological course of the symptoms. “Development” includes occurrence, recurrence, and onset. As used herein “onset” or “occurrence” of a cancer includes initial onset and/or recurrence.

In some embodiments, the anti-cancer therapeutic agent (e.g., an antibody) described herein is administered to a subject in need of the treatment at an amount sufficient to reduce cancer (e.g., tumor) growth by at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or greater). In some embodiments, the anti-cancer therapeutic agent (e.g., an antibody) described herein is administered to a subject in need of the treatment at an amount sufficient to reduce cancer cell number or tumor size by at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more). In other embodiments, the anti-cancer therapeutic agent is administered in an amount effective in altering cancer type. Alternatively, the anti-cancer therapeutic agent is administered in an amount effective in reducing tumor formation or metastasis.

Conventional methods, known to those of ordinary skill in the art of medicine, may be used to administer the anti-cancer therapeutic agent to the subject, depending upon the type of disease to be treated or the site of the disease. The anti-cancer therapeutic agent can also be administered via other conventional routes, e.g., administered orally, parenterally, by inhalation spray, topically, rectally, nasally, buccally, vaginally or via an implanted reservoir. The term “parenteral” as used herein includes subcutaneous, intracutaneous, intravenous, intramuscular, intraarticular, intraarterial, intrasynovial, intrasternal, intrathecal, intralesional, and intracranial injection or infusion techniques. In addition, an anti-cancer therapeutic agent may be administered to the subject via injectable depot routes of administration such as using 1-, 3-, or 6-month depot injectable or biodegradable materials and methods.

Injectable compositions may contain various carriers such as vegetable oils, dimethylactamide, dimethyformamide, ethyl lactate, ethyl carbonate, isopropyl myristate, ethanol, and polyols (e.g., glycerol, propylene glycol, liquid polyethylene glycol, and the like). For intravenous injection, water soluble anti-cancer therapeutic agents can be administered by the drip method, whereby a pharmaceutical formulation containing the antibody and a physiologically acceptable excipients is infused. Physiologically acceptable excipients may include, for example, 5% dextrose, 0.9% saline, Ringer's solution, and/or other suitable excipients. Intramuscular preparations, e.g., a sterile formulation of a suitable soluble salt form of the anti-cancer therapeutic agent, can be dissolved and administered in a pharmaceutical excipient such as Water-for-Injection, 0.9% saline, and/or 5% glucose solution.

In one embodiment, an anti-cancer therapeutic agent is administered via site-specific or targeted local delivery techniques. Examples of site-specific or targeted local delivery techniques include various implantable depot sources of the agent or local delivery catheters, such as infusion catheters, an indwelling catheter, or a needle catheter, synthetic grafts, adventitial wraps, shunts and stents or other implantable devices, site specific carriers, direct injection, or direct application. See, e.g., PCT Publication No. WO 00/53211 and U.S. Pat. No. 5,981,568, the contents of each of which are incorporated by reference herein for this purpose.

Targeted delivery of therapeutic compositions containing an antisense polynucleotide, expression vector, or subgenomic polynucleotides can also be used. Receptor-mediated DNA delivery techniques are described in, for example, Findeis et al., Trends Biotechnol. (1993) 11:202; Chiou et al., Gene Therapeutics: Methods And Applications Of Direct Gene Transfer (J. A. Wolff, ed.) (1994); Wu et al., J. Biol. Chem. (1988) 263:621; Wu et al., J. Biol. Chem. (1994) 269:542; Zenke et al., Proc. Natl. Acad. Sci. USA (1990) 87:3655; Wu et al., J. Biol. Chem. (1991) 266:338. The contents of each of the foregoing are incorporated by reference herein for this purpose.

Therapeutic compositions containing a polynucleotide may be administered in a range of about 100 ng to about 200 mg of DNA for local administration in a gene therapy protocol. In some embodiments, concentration ranges of about 500 ng to about 50 mg, about 1 μg to about 2 mg, about 5 μg to about 500 μg, and about 20 μg to about 100 μg of DNA or more can also be used during a gene therapy protocol.

Therapeutic polynucleotides and polypeptides can be delivered using gene delivery vehicles. The gene delivery vehicle can be of viral or non-viral origin (e.g., Jolly, Cancer Gene Therapy (1994) 1:51; Kimura, Human Gene Therapy (1994) 5:845; Connelly, Human Gene Therapy (1995) 1:185; and Kaplitt, Nature Genetics (1994) 6:148). The contents of each of the foregoing are incorporated by reference herein for this purpose. Expression of such coding sequences can be induced using endogenous mammalian or heterologous promoters and/or enhancers. Expression of the coding sequence can be either constitutive or regulated.

Viral-based vectors for delivery of a desired polynucleotide and expression in a desired cell are well known in the art. Exemplary viral-based vehicles include, but are not limited to, recombinant retroviruses (see, e.g., PCT Publication Nos. WO 90/07936; WO 94/03622; WO 93/25698; WO 93/25234; WO 93/11230; WO 93/10218; WO 91/02805; U.S. Pat. Nos. 5,219,740 and 4,777,127; GB Patent No. 2,200,651; and EP Patent No. 0 345 242), alphavirus-based vectors (e.g., Sindbis virus vectors, Semliki forest virus (ATCC VR-67; ATCC VR-1247), Ross River virus (ATCC VR-373; ATCC VR-1246) and Venezuelan equine encephalitis virus (ATCC VR-923; ATCC VR-1250; ATCC VR 1249; ATCC VR-532)), and adeno-associated virus (AAV) vectors (see, e.g., PCT Publication Nos. WO 94/12649, WO 93/03769; WO 93/19191; WO 94/28938; WO 95/11984 and WO 95/00655). Administration of DNA linked to killed adenovirus as described in Curiel, Hum. Gene Ther. (1992) 3:147 can also be employed. The contents of each of the foregoing are incorporated by reference herein for this purpose.

Non-viral delivery vehicles and methods can also be employed, including, but not limited to, polycationic condensed DNA linked or unlinked to killed adenovirus alone (see, e.g., Curiel, Hum. Gene Ther. (1992) 3:147); ligand-linked DNA (see, e.g., Wu, J. Biol. Chem. (1989) 264:16985); eukaryotic cell delivery vehicles cells (see, e.g., U.S. Pat. No. 5,814,482; PCT Publication Nos. WO 95/07994; WO 96/17072; WO 95/30763; and WO 97/42338) and nucleic charge neutralization or fusion with cell membranes. Naked DNA can also be employed. Exemplary naked DNA introduction methods are described in PCT Publication No. WO 90/11092 and U.S. Pat. No. 5,580,859. Liposomes that can act as gene delivery vehicles are described in U.S. Pat. No. 5,422,120; PCT Publication Nos. WO 95/13796; WO 94/23697; WO 91/14445; and EP Patent No. 0524968. Additional approaches are described in Philip, Mol. Cell. Biol. (1994) 14:2411, and in Woffendin, Proc. Natl. Acad. Sci. (1994) 91:1581. The contents of each of the foregoing are incorporated by reference herein for this purpose.

It is also apparent that an expression vector can be used to direct expression of any of the protein-based anti-cancer therapeutic agents (e.g., anti-cancer antibody). For example, peptide inhibitors that are capable of blocking (from partial to complete blocking) a cancer-causing biological activity are known in the art.

In some embodiments, more than one anti-cancer therapeutic agent, such as an antibody and a small molecule inhibitory compound, may be administered to a subject in need of the treatment. The agents may be of the same type or different types from each other. At least one, at least two, at least three, at least four, or at least five different agents may be co-administered. Generally anti-cancer agents for administration have complementary activities that do not adversely affect each other. Anti-cancer therapeutic agents may also be used in conjunction with other agents that serve to enhance and/or complement the effectiveness of the agents.

Treatment efficacy can be assessed by methods well-known in the art, e.g., monitoring tumor growth or formation in a patient subjected to the treatment. Alternatively or in addition to, treatment efficacy can be assessed by monitoring tumor type over the course of treatment (e.g., before, during, and after treatment).

A subject having cancer may be treated using any combination of anti-cancer therapeutic agents or one or more anti-cancer therapeutic agents and one or more additional therapies (e.g., surgery and/or radiotherapy). The term combination therapy, as used herein, embraces administration of more than one treatment (e.g., an antibody and a small molecule or an antibody and radiotherapy) in a sequential manner, that is, wherein each therapeutic agent is administered at a different time, as well as administration of these therapeutic agents, or at least two of the agents or therapies, in a substantially simultaneous manner.

Sequential or substantially simultaneous administration of each agent or therapy can be affected by any appropriate route including, but not limited to, oral routes, intravenous routes, intramuscular, subcutaneous routes, and direct absorption through mucous membrane tissues. The agents or therapies can be administered by the same route or by different routes. For example, a first agent (e.g., a small molecule) can be administered orally, and a second agent (e.g., an antibody) can be administered intravenously.

As used herein, the term “sequential” means, unless otherwise specified, characterized by a regular sequence or order, e.g., if a dosage regimen includes the administration of an antibody and a small molecule, a sequential dosage regimen could include administration of the antibody before, simultaneously, substantially simultaneously, or after administration of the small molecule, but both agents will be administered in a regular sequence or order. The term “separate” means, unless otherwise specified, to keep apart one from the other. The term “simultaneously” means, unless otherwise specified, happening or done at the same time, i.e., the agents are administered at the same time. The term “substantially simultaneously” means that the agents are administered within minutes of each other (e.g., within 10 minutes of each other) and intends to embrace joint administration as well as consecutive administration, but if the administration is consecutive it is separated in time for only a short period (e.g., the time it would take a medical practitioner to administer two agents separately). As used herein, concurrent administration and substantially simultaneous administration are used interchangeably. Sequential administration refers to temporally separated administration of the agents or therapies described herein.

Combination therapy can also embrace the administration of the anti-cancer therapeutic agent (e.g., an antibody) in further combination with other biologically active ingredients (e.g., a vitamin) and non-drug therapies (e.g., surgery or radiotherapy).

It should be appreciated that any combination of anti-cancer therapeutic agents may be used in any sequence for treating a cancer. The combinations described herein may be selected on the basis of a number of factors, which include but are not limited to reducing tumor formation or tumor growth, and/or alleviating at least one symptom associated with the cancer, or the effectiveness for mitigating the side effects of another agent of the combination. For example, a combined therapy as provided herein may reduce any of the side effects associated with each individual members of the combination, for example, a side effect associated with an administered anti-cancer agent.

In some embodiments, an anti-cancer therapeutic agent is an antibody, an immunotherapy, a radiation therapy, a surgical therapy, and/or a chemotherapy.

Examples of the antibody anti-cancer agents include, but are not limited to, alemtuzumab (Campath), trastuzumab (Herceptin), Ibritumomab tiuxetan (Zevalin), Brentuximab vedotin (Adcetris), Ado-trastuzumab emtansine (Kadcyla), blinatumomab (Blincyto), Bevacizumab (Avastin), Cetuximab (Erbitux), ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab (Imfinzi), and panitumumab (Vectibix).

Examples of an immunotherapy include, but are not limited to, a PD-1 inhibitor or a PD-L1 inhibitor, a CTLA-4 inhibitor, adoptive cell transfer, therapeutic cancer vaccines, oncolytic virus therapy, T-cell therapy, and immune checkpoint inhibitors.

Examples of radiation therapy include, but are not limited to, ionizing radiation, gamma-radiation, neutron beam radiotherapy, electron beam radiotherapy, proton therapy, brachytherapy, systemic radioactive isotopes, and radiosensitizers.

Examples of a surgical therapy include, but are not limited to, a curative surgery (e.g., tumor removal surgery), a preventive surgery, a laparoscopic surgery, and a laser surgery.

Examples of the chemotherapeutic agents include, but are not limited to, Carboplatin or Cisplatin, Docetaxel, Gemcitabine, Nab-Paclitaxel, Paclitaxel, Pemetrexed, and Vinorelbine.

Additional examples of chemotherapy include, but are not limited to, Platinating agents, such as Carboplatin, Oxaliplatin, Cisplatin, Nedaplatin, Satraplatin, Lobaplatin, Triplatin, Tetranitrate, Picoplatin, Prolindac, Aroplatin and other derivatives; Topoisomerase I inhibitors, such as Camptothecin, Topotecan, irinotecan/SN38, rubitecan, Belotecan, and other derivatives; Topoisomerase II inhibitors, such as Etoposide (VP-16), Daunorubicin, a doxorubicin agent (e.g., doxorubicin, doxorubicin hydrochloride, doxorubicin analogs, or doxorubicin and salts or analogs thereof in liposomes), Mitoxantrone, Aclarubicin, Epirubicin, Idarubicin, Amrubicin, Amsacrine, Pirarubicin, Valrubicin, Zorubicin, Teniposide and other derivatives; Antimetabolites, such as Folic family (Methotrexate, Pemetrexed, Raltitrexed, Aminopterin, and relatives or derivatives thereof); Purine antagonists (Thioguanine, Fludarabine, Cladribine, 6-Mercaptopurine, Pentostatin, clofarabine, and relatives or derivatives thereof) and Pyrimidine antagonists (Cytarabine, Floxuridine, Azacitidine, Tegafur, Carmofur, Capacitabine, Gemcitabine, hydroxyurea, 5-Fluorouracil (5FU), and relatives or derivatives thereof); Alkylating agents, such as Nitrogen mustards (e.g., Cyclophosphamide, Melphalan, Chlorambucil, mechlorethamine, Ifosfamide, mechlorethamine, Trofosfamide, Prednimustine, Bendamustine, Uramustine, Estramustine, and relatives or derivatives thereof); nitrosoureas (e.g., Carmustine, Lomustine, Semustine, Fotemustine, Nimustine, Ranimustine, Streptozocin, and relatives or derivatives thereof); Triazenes (e.g., Dacarbazine, Altretamine, Temozolomide, and relatives or derivatives thereof); Alkyl sulphonates (e.g., Busulfan, Mannosulfan, Treosulfan, and relatives or derivatives thereof); Procarbazine; Mitobronitol, and Aziridines (e.g., Carboquone, Triaziquone, ThioTEPA, triethylenemalamine, and relatives or derivatives thereof); Antibiotics, such as Hydroxyurea, Anthracyclines (e.g., doxorubicin agent, daunorubicin, epirubicin and relatives or derivatives thereof); Anthracenediones (e.g., Mitoxantrone and relatives or derivatives thereof); Streptomyces family antibiotics (e.g., Bleomycin, Mitomycin C, Actinomycin, and Plicamycin); and ultraviolet light.

Computer Implementation

An illustrative implementation of a computer system 1000 that may be used in connection with any of the embodiments of the technology described herein (e.g., such as the methods of FIGS. 4A-C) is shown in FIG. 10 . The computer system 1000 includes one or more processors 1010 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 1020 and one or more non-volatile storage media 1030). The processor 1010 may control writing data to and reading data from the memory 1020 and the non-volatile storage device 1030 in any suitable manner, as the aspects of the technology described herein are not limited to any particular techniques for writing or reading data. To perform any of the functionality described herein, the processor 1010 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 1020), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 1010.

Computing device 1000 may also include a network input/output (I/O) interface 1040 via which the computing device may communicate with other computing devices (e.g., over a network), and may also include one or more user I/O interfaces 1050, via which the computing device may provide output to and receive input from a user. The user I/O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and/or various other types of I/O devices.

The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed among multiple computing devices. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-described functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above.

In this respect, it should be appreciated that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible, non-transitory computer-readable storage medium) encoded with a computer program (i.e., a plurality of executable instructions) that, when executed on one or more processors, performs the above-described functions of one or more embodiments. The computer-readable medium may be transportable such that the program stored thereon can be loaded onto any computing device to implement aspects of the techniques described herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs any of the above-described functions, is not limited to an application program running on a host computer. Rather, the terms computer program and software are used herein in a generic sense to reference any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be employed to program one or more processors to implement aspects of the techniques described herein.

The foregoing description of implementations provides illustration and description but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the implementations. In other implementations the methods depicted in these figures may include fewer operations, different operations, differently ordered operations, and/or additional operations. Further, non-dependent blocks may be performed in parallel.

It will be apparent that example aspects, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. Further, certain portions of the implementations may be implemented as a “module” that performs one or more functions. This module may include hardware, such as a processor, an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA), or a combination of hardware and software.

Having thus described several aspects and embodiments of the technology set forth in the disclosure, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be within the spirit and scope of the technology described herein. For example, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the embodiments described herein. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described. In addition, any combination of two or more features, systems, articles, materials, kits, and/or methods described herein, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

The above-described embodiments can be implemented in any of numerous ways. One or more aspects and embodiments of the present disclosure involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other device) to perform, or control performance of, the processes or methods. In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects as described above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular fashion among a number of different computers or processors to implement various aspects of the present disclosure.

Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer, as non-limiting examples. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smartphone, a tablet, or any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible formats.

Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

Also, as described, some aspects may be embodied as one or more methods. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively.

The terms “approximately,” “substantially,” and “about” may be used to mean within ±20% of a target value in some embodiments, within ±10% of a target value in some embodiments, within ±5% of a target value in some embodiments, within ±2% of a target value in some embodiments. The terms “approximately,” “substantially,” and “about” may include the target value. 

What is claimed is:
 1. A method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: using at least one computer hardware processor to perform: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
 2. The method of claim 1, wherein the RNA expression data further comprises third RNA expression data for a third set of genes different from the first and second sets of genes, wherein the hierarchy of molecular categories further comprises a third molecular category that is a child of the parent molecular category in the hierarchy of molecular categories, wherein the hierarchy of RNA-based machine learning classifiers further comprises a third RNA-based machine learning classifier corresponding to the third molecular category, wherein the processing further comprises processing the third RNA expression data using the third RNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the third output.
 3. The method of claim 1 or any other preceding claim, wherein the RNA expression data further comprises fourth RNA expression data for a fourth set of genes different from the first and second sets of genes, wherein the hierarchy of molecular categories further comprises a fourth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of RNA-based machine learning classifiers further comprises a fourth RNA-based machine learning classifier corresponding to the fourth molecular category, wherein the processing further comprises processing the fourth RNA expression data using the fourth RNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.
 4. The method of claim 3, wherein the RNA expression data further comprises fifth RNA expression data for a fifth set of genes different from the first, second, and fourth sets of genes, wherein the hierarchy of molecular categories further comprises a fifth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of RNA-based machine learning classifiers further comprises a fifth RNA-based machine learning classifier corresponding to the fifth molecular category, wherein the processing further comprises processing the fifth RNA expression data using the fifth RNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.
 5. The method of claim 1 or any other preceding claim, wherein the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category.
 6. The method of claim 1 or any other preceding claim, wherein the parent molecular category is a breast cancer molecular category, wherein the first molecular category is a basal breast cancer molecular category, and wherein the second molecular category is a non-basal breast cancer molecular category.
 7. The method of claim 1 or any other preceding claim, wherein the parent molecular category is a molecular category selected from Table 2, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories shown in FIGS. 7A-1, 7A-2, and 7A-3 .
 8. The method of claim 1 or any other preceding claim, wherein processing the first RNA expression data using the first RNA-based machine learning classifier comprises: obtaining first RNA features from the first RNA expression data; and applying the first RNA-based machine learning classifier to the first RNA features to obtain the first output.
 9. The method of claim 8, wherein the first RNA expression data comprises first expression levels for the first set of genes, wherein obtaining the first RNA features from the first RNA expression data comprises ranking at least some genes in the first set of genes based on the first expression levels to obtain a first gene ranking, the first gene ranking including values identifying relative ranks of the at least some genes in the gene ranking, wherein the values are different from the first expression levels, and wherein applying the first RNA-based machine learning classifier to the first RNA features comprises applying the first RNA-based machine learning classifier to the first gene ranking to obtain the first output.
 10. The method of claim 1 or any other preceding claim, wherein processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output comprises processing the first RNA expression data to obtain a first probability that the first molecular category is a first candidate molecular category for the biological sample, and wherein processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output comprises processing the second RNA expression data to obtain a second probability that the second molecular category is a second candidate molecular category for the biological sample.
 11. The method of claim 10, wherein identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold; and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold.
 12. The method of claim 11, further comprising excluding the first molecular category from the at least one candidate molecular category identified for the biological sample when the first probability does not exceed the threshold.
 13. The method of claim 10, wherein identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to the second probability; and identifying the first molecular category as a candidate molecular category of the at least one candidate molecular category for the biological sample when the first probability exceeds the second probability.
 14. The method of claim 1 or any other preceding claim, wherein the first molecular category is a molecular category selected from molecular categories listed in Table
 2. 15. The method of claim 1 or any other preceding claim, wherein the first set of genes comprises at least 10 genes listed in Table 3 corresponding to the first molecular category.
 16. The method of claim 1 or any other preceding claim, wherein the first molecular category is associated with at least one international classification of diseases (ICD) code.
 17. The method of claim 1 or any other preceding claim, further comprising: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject; and processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to the hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs, wherein the hierarchy of DNA-based machine learning classifiers is different from the hierarchy of RNA-based machine learning classifiers, wherein the identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the DNA-based machine learning classifier outputs.
 18. The method of claim 17, wherein processing the DNA expression data comprises: obtaining one or more DNA features using the DNA expression data; and applying at least one DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers to at least some of the DNA features to obtain the DNA-based machine learning classifier outputs.
 19. The method of claim 18, wherein the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene.
 20. The method of any of claims 18-19, wherein the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene.
 21. The method of any of claims 18-20, wherein the one or more DNA features comprise a feature indicating tumor mutational burden for the biological sample.
 22. The method of any of claims 18-21, wherein the one or more DNA features comprise one or more features indicating a normalized copy number for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.
 23. The method of any of claims 18-22, wherein the one or more DNA features comprise one or more features indicating loss of heterozygosity (LOH) for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.
 24. The method of any of claims 18-23, wherein the one or more DNA features comprise one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes.
 25. The method of any of claims 18-24, wherein the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene of the respective plurality of genes.
 26. The method of any of claims 18-25, wherein the one or more DNA features comprises a feature indicating ploidy for the biological sample.
 27. The method of any of claims 18-26, wherein the one or more DNA features comprise a indicating whether the DNA expression data indicates presence of microsatellite instability (MSI).
 28. The method of any of claims 18-27, wherein the one or more DNA features comprise at least ten features listed in Table
 5. 29. The method of any of claims 17-28, wherein the identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of a purity of the biological sample and/or data indicative of a site form which the biological sample was obtained.
 30. The method of any of claims 17-29, wherein the hierarchy of DNA-based machine learning classifiers comprises at least 10 DNA-based machine learning classifiers.
 31. The method of any of claims 17-30, wherein a first DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 32. The method of claim 17, wherein each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, and a logistic regression classifier.
 33. The method of claim 1 or any other preceding claim, wherein the method further comprises: receiving an indication of a clinical diagnosis of the biological sample; and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample.
 34. The method of claim 1 or any other preceding claim, further comprising: generating, using the hierarchy of molecular categories, a graphical user interface (GUI) including a visualization indicating the at least one molecular category identified for the biological sample.
 35. The method of claim 1 or any other preceding claim, wherein the first molecular category of the hierarchy of molecular categories is one of a neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma.
 36. The method of claim 1 or any other preceding claim, wherein the hierarchy of RNA-based machine learning classifiers comprises at least 10 RNA-based machine learning classifiers.
 37. The method of claim 1 or any other preceding claim, wherein the first RNA-based machine learning classifier is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 38. The method of claim 1 or any other preceding claim, wherein each RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 39. The method of claim 1 or any other preceding claim, wherein the first RNA expression data comprises expression levels for between 20 and 300 genes.
 40. The method of claim 1 or any other preceding claim, wherein the subject has, is suspected of having or is at risk for having cancer.
 41. The method of claim 1 or any other preceding claim, wherein the biological sample is a sample of a cancer of unknown primary (CUP) tumor.
 42. The method of claim 1 or any other preceding claim, further comprising: identifying at least one anti-cancer therapy for the subject based on the identified at least one molecular category.
 43. The method of claim 42, further comprising: administering the at least one anti-cancer therapy.
 44. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
 45. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the RNA expression data comprises first RNA expression data for a first set of genes and second RNA expression data for a second set of genes different from the first set of genes; processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of RNA-based machine learning classifiers comprising first and second RNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first RNA expression data using the first RNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second RNA expression data using the second RNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the RNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
 46. A method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: using at least one computer hardware processor to perform: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data and second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
 47. The method of claim 46, wherein the DNA expression data further comprises third DNA expression data, wherein the hierarchy of molecular categories further comprises a third molecular category that is a child of the parent molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a third DNA-based machine learning classifier corresponding to the third molecular category, wherein the processing further comprises processing the third DNA expression data using the third DNA-based machine learning classifier to obtain a third output indicative of whether the third molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the third output.
 48. The method of claim 46 or any other preceding claim, wherein the DNA expression data further comprises fourth DNA expression data, wherein the hierarchy of molecular categories further comprises a fourth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a fourth DNA-based machine learning classifier corresponding to the fourth molecular category, wherein the processing further comprises processing the fourth DNA expression data using the fourth DNA-based machine learning classifier to obtain a fourth output indicative of whether the fourth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fourth output.
 49. The method of claim 48, wherein the DNA expression data further comprises fifth DNA expression data, wherein the hierarchy of molecular categories further comprises a fifth molecular category that is a child of the first molecular category in the hierarchy of molecular categories, wherein the hierarchy of DNA-based machine learning classifiers further comprises a fifth DNA-based machine learning classifier corresponding to the fifth molecular category, wherein the processing further comprises processing the fifth DNA expression data using the fifth DNA-based machine learning classifier to obtain a fifth output indicative of whether the fifth molecular category is a candidate molecular category for the biological sample, and wherein identifying the at least one candidate molecular category for the biological sample is performed using the fifth output.
 50. The method of claim 46 or any other preceding claim, wherein the parent molecular category is a solid neoplasm molecular category, the first molecular category is an adenocarcinoma molecular category, and the second molecular category is a sarcoma molecular category.
 51. The method of claim 46 or any other preceding claim, wherein the parent molecular category is a breast cancer molecular category, the first molecular category is a basal breast cancer molecular category, and the second molecular category is a non-basal molecular category.
 52. The method of claim 46 or any other preceding claim, wherein the parent molecular category is a molecular category selected from Table 2, and the first and second molecular categories are children of the parent molecular category in the hierarchy of categories shown in FIGS. 7A-1, 7A-2, and 7A-3 .
 53. The method of claim 46 or any other preceding claim, wherein processing the first DNA expression data using the first DNA-based machine learning classifier comprises: obtaining one or more first DNA features from the first DNA expression data; and applying the first DNA-based machine learning classifier to the first DNA features to obtain the first output.
 54. The method of claim 53, wherein the one or more first DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a pathogenic mutation for the gene.
 55. The method of any one of claims 53-54, wherein the one or more first DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a hotspot mutation for the gene.
 56. The method of any one of claims 53-55, wherein the one or more first DNA features comprise a feature indicating tumor mutational burden for the biological sample.
 57. The method of any one claims 53-56, wherein the one or more DNA features comprise one or more features indicating a normalized copy number for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.
 58. The method of any one of claims 53-57, wherein the one or more DNA features comprise one or more features indicating loss of heterozygosity (LOH) for each chromosome segment of a respective set of one or more chromosome segments for which expression data is included in the DNA expression data.
 59. The method of any one of claims 53-58, wherein the one or more DNA features comprise one or more features indicating whether the DNA expression data indicates presence of one or more protein coding genes.
 60. The method of any one of claims 53-59, wherein the one or more DNA features comprise one or more features indicating, for each gene of a respective set of one or more genes, whether the DNA expression data indicates presence of a fusion with another gene of the respective plurality of genes.
 61. The method of any one of claims 53-60, wherein the one or more DNA features comprises a feature indicating ploidy for the biological sample.
 62. The method of any one of claims 53-61, wherein the one or more DNA features comprise a indicating whether the DNA expression data indicates presence of microsatellite instability (MSI).
 63. The method of any one of claims 53-62, wherein the one or more first DNA features comprise at least 10 features listed Table 5 corresponding to the first molecular category.
 64. The method of claim 46 or any other preceding claim, wherein processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output comprises processing the first DNA expression data to obtain a first probability that the first molecular category is a first candidate molecular category for the biological sample, and wherein processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output comprises processing the second DNA expression data to obtain a second probability that the second molecular category is a second candidate molecular category for the biological sample.
 65. The method of claim 64, wherein identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to a threshold; and including the first molecular category in the at least one candidate molecular category identified for the biological sample when the first probability exceeds the threshold.
 66. The method of claim 65, further comprising excluding the first molecular category from the at least one candidate molecular category identified for the biological sample when the first probability does not exceed the threshold.
 67. The method of claim 64, wherein identifying the at least one candidate molecular category for the biological sample comprises: comparing the first probability to the second probability; and identifying the first molecular category as a candidate molecular category of the at least one candidate molecular category for the biological sample when the first probability exceeds the second probability.
 68. The method of claim 46, wherein the first molecular category is a molecular category selected from molecular categories listed in Table
 2. 69. The method of claim 46, wherein the first molecular category is associated with at least one international classification of diseases (ICD) code.
 70. The method of claim 46 or any other preceding claim, further comprising: obtaining RNA expression data previously obtained by processing the biological sample obtained from the subject; and processing the RNA expression data using a hierarchy of RNA-based machine learning classifiers corresponding to the hierarchy of molecular categories to obtain RNA-based machine learning classifier outputs, wherein the hierarchy of RNA-based machine learning classifiers is different from the hierarchy of DNA-based machine learning classifiers, wherein the identifying of the at least one candidate molecular category for the biological sample is performed also using at least some of the RNA-based machine learning classifier outputs.
 71. The method of claim 70, wherein processing the RNA expression data comprises: obtaining RNA features using the RNA expression data; and applying at least one RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers to at least some of the RNA features to obtain the RNA-based machine learning classifier outputs.
 72. The method of claim 71, wherein the RNA expression data comprises expression levels for at least one set of genes, wherein obtaining the RNA features using the RNA expression data comprises ranking genes in the at least one set of genes based on the expression levels to obtain at least one gene ranking, the at least one gene ranking including values identifying relative ranks of the genes in the at least one gene ranking, wherein the values are different from the expression levels, and wherein applying the at least one RNA-based machine learning classifier to the at least some of the RNA features comprises applying the RNA-based machine learning classifier to the at least one gene ranking to obtain the RNA-based machine learning classifier outputs.
 73. The method of any one of claims 70-72, wherein the identifying of the at least one candidate molecular category for the biological sample is performed based on data indicative of a purity of the biological sample and/or based on data indicative of a site from which the biological sample was obtained.
 74. The method of any one of claims 70-73, wherein the hierarchy of RNA-based machine learning classifiers comprises at least 10 RNA-based machine learning classifiers.
 75. The method of any one of claims 70-74, wherein a first RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 76. The method of claim 75, wherein each RNA-based machine learning classifier of the hierarchy of RNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 77. The method of any one of claims 70-76, wherein the RNA expression data comprises expression levels for between 20 and 300 genes.
 78. The method of claim 46 or any other preceding claim, wherein the method further comprises: receiving an indication of a clinical diagnosis of the biological sample; and determining an accuracy of the clinical diagnosis based on the at least one candidate molecular category identified for the biological sample.
 79. The method of claim 46 or any other preceding claim, further comprising: generating, using the hierarchy of molecular categories, a graphical user interface (GUI) including a visualization indicating the at least one molecular category identified for the biological sample.
 80. The method of claim 46 or any other preceding claim, wherein the first molecular category of the hierarchy of molecular categories is one of neoplasm, hematologic neoplasm, melanoma, sarcoma, mesothelioma, neuroendocrine, squamous cell carcinoma, adenocarcinoma, glioma, testicular germ cell tumor, pheochromocytoma, cervical squamous cell carcinoma, liver neoplasm, lung adenocarcinoma, high grade glioma isocitrate dehydrogenase (IDH) mutant, thyroid neoplasm, squamous cell lung adenocarcinoma, thymoma, prostate adenocarcinoma, urinary bladder urothelial carcinoma, oligodendroglioma, squamous cell carcinoma of the head and neck, gastrointestinal adenocarcinoma, gynecological cancer, renal cell carcinoma, astrocytoma, pancreatic adenocarcinoma, stomach adenocarcinoma, pancreatic adenocarcinoma, breast cancer, ovarian cancer, uterine corpus endometrial carcinoma, non-clear cell carcinoma, clear cell carcinoma, basal breast cancer, non-basal breast cancer, papillary renal cell carcinoma, and chromophobe renal cell carcinoma.
 81. The method of claim 46 or any other preceding claim, wherein the hierarchy of DNA-based machine learning classifiers comprises at least 10 DNA-based machine learning classifiers.
 82. The method of claim 46 or any other preceding claim, wherein the first DNA-based machine learning classifier is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 83. The method of claim 46 or any other preceding claim, wherein each DNA-based machine learning classifier of the hierarchy of DNA-based machine learning classifiers is a gradient-boosted decision tree classifier, a neural network classifier, or a logistic regression classifier.
 84. The method of claim 46 or any other preceding claim, wherein the subject has, is suspected of having or is at risk for having cancer.
 85. The method of claim 46 or any other preceding claim, wherein the biological sample is a sample of a cancer of unknown primary (CUP) tumor.
 86. The method of claim 46 or any other preceding claim, further comprising: identifying at least one anti-cancer therapy for the subject based on the identified at least one molecular category.
 87. The method of claim 46 or any other preceding claim, further comprising: administering the at least one anti-cancer therapy.
 88. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
 89. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for identifying at least one candidate molecular category for a biological sample obtained from a subject, the method comprising: obtaining DNA expression data previously obtained by processing the biological sample obtained from the subject, wherein the DNA expression data comprises first DNA expression data and second DNA expression data; processing the DNA expression data using a hierarchy of DNA-based machine learning classifiers corresponding to a hierarchy of molecular categories to obtain DNA-based machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of DNA-based machine learning classifiers comprising first and second DNA-based machine learning classifiers corresponding to the first and second molecular categories, the processing comprising: processing the first DNA expression data using the first DNA-based machine learning classifier to obtain the first output indicative of whether the first molecular category is a candidate molecular category for the biological sample; processing the second DNA expression data using the second DNA-based machine learning classifier to obtain the second output indicative of whether the second molecular category is a candidate molecular category for the biological sample; and identifying, using at least some of the DNA-based machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample. 